Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
  • A61B5/0077—Devices for viewing the surface of the body, e.g. camera, magnifying lens
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/0002—Inspection of images, e.g. flaw detection
  • G06T7/0012—Biomedical image inspection
  • G06T7/0014—Biomedical image inspection using an image reference approach
  • G06T7/0016—Biomedical image inspection using an image reference approach involving temporal comparison
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
  • A61B5/024—Measuring pulse rate or heart rate
  • A61B5/02416—Measuring pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F18/00—Pattern recognition
  • G06F18/20—Analysing
  • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
  • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
  • G06F18/2134—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on separation criteria, e.g. independent component analysis
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F18/00—Pattern recognition
  • G06F18/20—Analysing
  • G06F18/22—Matching criteria, e.g. proximity measures
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/20—Image preprocessing
  • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/40—Extraction of image or video features
  • G06V10/56—Extraction of image or video features relating to colour
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
  • G06V10/762—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
  • G06V10/763—Non-hierarchical techniques, e.g. based on statistics of modelling distributions
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
  • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
  • G06V10/7715—Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
  • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
  • G06V40/12—Fingerprints or palmprints
  • G06V40/1382—Detecting the live character of the finger, i.e. distinguishing from a fake or cadaver finger
  • G06V40/1388—Detecting the live character of the finger, i.e. distinguishing from a fake or cadaver finger using image processing
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
  • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
  • G06V40/15—Biometric patterns based on physiological signals, e.g. heartbeat, blood flow
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
  • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
  • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
  • G06V40/161—Detection; Localisation; Normalisation
  • G06V40/162—Detection; Localisation; Normalisation using pixel segmentation or colour matching
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/10—Image acquisition modality
  • G06T2207/10016—Video; Image sequence
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/10—Image acquisition modality
  • G06T2207/10024—Color image
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/20—Special algorithmic details
  • G06T2207/20212—Image combination
  • G06T2207/20216—Image averaging
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/30—Subject of image; Context of image processing
  • G06T2207/30004—Biomedical image processing
  • G06T2207/30076—Plethysmography
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
  • G06V40/40—Spoof detection, e.g. liveness detection
  • G06V40/45—Detection of the body part being alive

Definitions

• the invention relates to an apparatus and method for determining a pulse signal from a video sequence.
• remote PPG remote photoplethysmography
• rPPG remote photoplethysmography
• the invisible physiological features e.g. pulse
• the invisible physiological features can better differentiate humans from non-humans in a video sequence.
• Figure 1 provides two examples of how a living tissue detection technique should successfully operate.
• a human face and an artificial face are present face on to the camera, and only the human face should be identified (despite the artificial face having similar physical appearance features to the human face), as indicated by the dashed box and outline of the area corresponding to living skin tissue.
• a human face and an artificial face are present side on to the camera, and only the human face should be identified.
• a method for determining a pulse signal from a video sequence comprising obtaining a video sequence, the video sequence comprising a plurality of image frames; forming a plurality of video sub-sequences, each video sub-sequence comprising a frame segment from each image frame in a subset of the image frames, wherein each subset comprises a plurality of image frames, wherein each image frame is divided into a plurality of frame segments, wherein each frame segment is a group of neighboring pixels in the image frame; for a first video sub-sequence formed from frame segments from a first subset of image frames, comparing a representative value for the first video sub-sequence to representative values for video sub-sequences formed from frame segments from a second subset of image frames; concatenating the first video sub-sequence to a second video sub-sequence formed from frame segments from the second subset of image frames based on the comparison of representative values; and
• the method further comprising the steps of for the second video sub-sequence, comparing a representative value for the second video sub- sequence to representative values for video sub-sequences formed from frame segments from a third subset of image frames; and concatenating the first video sub-sequence and second video sub-sequence to a third video sub-sequence formed from frame segments from the third subset of image frames based on the comparison of representative values.
• the method further comprises repeating the steps of comparing and concatenating in order to concatenate video sub-sequences from each of a plurality of subsets of image frames.
• each subset of image frames comprises a respective set of consecutive image frames in the video sequence.
• each image frame is in more than one subset such that the subsets of image frames are overlapping.
• each subset comprises at least one image frame that is common to another subset.
• each image frame in the video sequence is in a respective subset of image frames.
• each subset of image frames comprises a respective set of three image frames in the video sequence.
• the first subset of image frames and the second subset of image frames comprise image frames that are adjacent in the video sequence.
• each frame segment is a group of neighboring pixels in the image frame.
• each image frame is divided into a plurality of frame segments by grouping pixels into frame segments based on color and spatial similarities of the pixels.
• each image frame is divided into a plurality of frame segments independently of the other image frames.
• each image frame in a subset is divided into a plurality of frame segments based on the content of the image frame and the other image frames in the subset.
• the step of forming the plurality of video sub- sequences comprises forming each video sub-sequence from frame segments in
• the step of forming the plurality of video sub- sequences comprises, for each video sub-sequence, selecting frame segments from each image frame in the subset of image frames such that a chromatic energy and/or spatial- distance energy between the frame segments in the video sub-sequence is minimized.
• the method further comprises the step of determining a representative value for each video sub-sequence.
• the step of determining a representative value for each video sub-sequence can comprise averaging the pixel values of pixels in the frame segments in the video sub-sequence.
• the step of averaging the pixel values can comprise weighting the pixel values of pixels in each frame segment, wherein the pixel values are weighted based on spatial position of the pixel in the frame segment and/or a difference in color with a pixel or group of pixels at or near the center of the frame segment; and averaging the weighted pixel values of pixels in a frame segment.
• the step of determining a representative value further comprises normalizing the average of the pixel values in the frame segment.
• the step of determining a representative value for each video sub-sequence comprises determining a difference between the averages of the pixel values of pixels in the frame segments in the video sub-sequence.
• the step of comparing comprises identifying the second video sub-sequence as a video sub-sequence formed from frame segments from the second subset of image frames that is similar or most similar to the first video sub-sequence.
• the step of comparing comprises identifying the second video sub-sequence as a video sub-sequence formed from frame segments from the second subset of image frames that is similar or most similar to the first video sub-sequence in spatial distance and/or representative value.
• the step of determining a pulse signal comprises determining the pulse signal from the representative values of the concatenated video sub- sequences.
• the steps of forming, determining a representative value, comparing, concatenating and determining a pulse signal are performed for image frames that are divided into a first plurality of frame segments; and wherein the method further comprises the steps of repeating the steps of forming, determining a representative value, comparing, concatenating and determining a pulse signal for the image frames in the video sequence when the image frames are divided into a second plurality of frame segments, the second plurality of frame segments comprising a different number of frame segments than the first plurality of frame segments.
• the method further comprises the steps of repeating the steps of comparing, concatenating and determining a pulse signal for other video sub- sequences formed from frame segments from the first subset of image frames to determine further pulse signals; and analyzing the pulse signals to identify areas of living skin tissue in the video sequence.
• the method further comprises the step of determining one or more physiological characteristics from one or more pulse signals associated with the identified areas of living skin tissue in the video sequence.
• a computer program product comprising a computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is caused to perform any of the methods described above.
• an apparatus for determining a pulse signal from a video sequence comprising a processing unit configured to obtain a video sequence, the video sequence comprising a plurality of image frames; form a plurality of video sub-sequences, each video sub-sequence comprising a frame segment from each image frame in a subset of the image frames, wherein each subset comprises a plurality of image frames, wherein each image frame is divided into a plurality of frame segments, wherein each frame segment is a group of neighboring pixels in the image frame; for a first video sub-sequence formed from frame segments from a first subset of image frames, comparing a representative value for the first video sub-sequence to representative values for video sub-sequences formed from frame segments from a second subset of image frames; concatenate the first video sub-sequence to a second video sub-sequence formed from frame segments from the second subset of image frames based on the comparison of representative
• the processing unit is further configured to, for the second video sub-sequence, compare a representative value for the second video sub- sequence to representative values for video sub-sequences formed from frame segments from a third subset of image frames; and concatenate the first video sub-sequence and second video sub-sequence to a third video sub-sequence formed from frame segments from the third subset of image frames based on the comparison of representative values.
• the processing unit is further configured to repeat the comparison and concatenation in order to concatenate video sub-sequences from each of a plurality of subsets of image frames.
• each subset of image frames comprises a respective set of consecutive image frames in the video sequence.
• each image frame is in more than one subset such that the subsets of image frames are overlapping.
• each subset comprises at least one image frame that is common to another subset.
• each image frame in the video sequence is in a respective subset of image frames.
• each subset of image frames comprises a respective set of three image frames in the video sequence.
• the first subset of image frames and the second subset of image frames comprise image frames that are adjacent in the video sequence.
• each frame segment is a group of neighboring pixels in the image frame.
• each image frame is divided into a plurality of frame segments by grouping pixels into frame segments based on color and spatial similarities of the pixels.
• each image frame is divided into a plurality of frame segments independently of the other image frames.
• each image frame in a subset is divided into a plurality of frame segments based on the content of the image frame and the other image frames in the subset.
• the processing unit is configured to form the plurality of video sub-sequences by forming each video sub-sequence from frame segments in corresponding spatial positions in the image frames in the subset of image frames.
• the processing unit is configured to form the plurality of video sub-sequences by, for each video sub-sequence, selecting frame segments from each image frame in the subset of image frames such that a chromatic energy and/or spatial-distance energy between the frame segments in the video sub-sequence is minimized.
• the processing unit is further configured to determine a representative value for each video sub-sequence.
• the processing unit is configured to determine a representative value for each video sub-sequence can by averaging the pixel values of pixels in the frame segments in the video sub-sequence.
• the processing unit is configured to average the pixel values by weighting the pixel values of pixels in each frame segment, wherein the pixel values are weighted based on spatial position of the pixel in the frame segment and/or a difference in color with a pixel or group of pixels at or near the center of the frame segment; and averaging the weighted pixel values of pixels in a frame segment.
• the processing unit is configured to determine a representative value by normalizing the average of the pixel values in the frame segment.
• the processing unit is configured to determine a representative value for each video sub-sequence by determining a difference between the averages of the pixel values of pixels in the frame segments in the video sub-sequence.
• the processing unit is configured to compare by identifying the second video sub-sequence as a video sub-sequence formed from frame segments from the second subset of image frames that is similar or most similar to the first video sub-sequence.
• the processing unit is configured to compare by identifying the second video sub-sequence as a video sub-sequence formed from frame segments from the second subset of image frames that is similar or most similar to the first video sub-sequence in spatial distance and/or representative value.
• the processing unit is configured to determine a pulse signal by determining the pulse signal from the representative values of the concatenated video sub-sequences.
• the processing unit is configured to form, determine a representative value, compare, concatenate and determine a pulse signal for image frames that are divided into a first plurality of frame segments; and the processing unit is further configured to repeat the forming, determining a representative value, comparing,
• the processing unit is further configured to repeat the comparing, concatenating and determining a pulse signal for other video sub-sequences formed from frame segments from the first subset of image frames to determine further pulse signals; and analyze the pulse signals to identify areas of living skin tissue in the video sequence.
• the processing unit is further configured to determine one or more physiological characteristics from one or more pulse signals associated with the identified areas of living skin tissue in the video sequence.
• Figure 1 illustrates the desired operation of a living skin tissue detection technique
• FIG. 2 is a block diagram of an apparatus according to an embodiment of the invention.
• Figure 3 is a flow chart illustrating a method according to an embodiment of the invention.
• Figures 4(a)-(d) illustrate how a pulse signal can be obtained from a video sequence
• Figure 4(a) illustrates how a video sequence is made up of a series of image frames
• Figure 4(b) illustrates how each of the image frames is divided into a plurality of frame segments
• Figure 4(c) illustrates how two video sub-sequences are formed using frame segments in the same spatial position within the image frames
• Figure 4(d) illustrates exemplary pulse signals for the two video subsequences so formed
• Figure 5 is a diagram illustrating the processing stages in the exemplary Voxel-Pulse-Spectral method
• Figure 6 illustrates segmentation of an image frame in three different scales
• Figure 7 illustrates four different measures of pairwise similarity and a resulting similarity matrix
• Figure 8 shows an example of similarity matrix decomposition using incremental sparse PCA.
• Figure 9 illustrates the projection of eigenvectors onto hierarchical voxels and a fused map indicating which parts of the video sequence correspond to living skin tissue.
• FIG. 2 An apparatus 2 that can be used to determine a pulse signal from a video sequences according to an embodiment of the invention is shown in Figure 2.
• the apparatus 2 can be used to identify living skin tissue from the determined pulse signal.
• the apparatus 2 comprises an imaging unit 4 that captures a video sequence over a period of time.
• the imaging unit 4 can be or comprise a camera, for example an RGB camera, that can be used for rPPG measurements.
• the imaging unit 4 provides a video sequence comprising a plurality of image frames to a processing unit 6.
• the processing unit 6 controls the operation of the apparatus 2 and can comprise one or more processors, multi-core processors or processing modules for implementing the living skin tissue identification techniques described herein.
• the processing unit 6 can be implemented as a plurality of processing modules, with each module being configured to perform a particular part or step of the living skin tissue identification techniques described herein.
• the apparatus 2 further comprises a memory unit 8 for storing computer readable program code that can be executed by the processing unit 6 to perform the method according to the invention.
• the memory unit 8 can also be used to store or buffer the video sequence from the imaging unit 4 before, during and after processing by the processing unit 6 and any intermediate products of the processing.
• the apparatus 2 can comprise a general-purpose computer (e.g. a desktop PC) with an integrated or separate imaging unit 4, or a portable computing device (e.g. a laptop, tablet or smart phone) that has an integrated or separate imaging unit 4.
• the apparatus 2 can be dedicated to the purpose of determining a pulse signal from a video sequence, identifying living skin tissue in a video sequence using the pulse signal, and/or for measuring physiological characteristics of a subject from rPPG signals extracted from areas of a video sequence identified as corresponding to living skin tissue.
• the apparatus 2 may comprise other or further components to those shown in Figure 2 and described above, such as a user interface that allows a subject to activate and/or operate the apparatus 2, and a power supply, such as a battery or connection to a mains power supply, for powering the apparatus 2.
• the user interface may comprise one or more components that allow a subject to interact and control the apparatus 2.
• the one or more user interface components could comprise a switch, a button or other control means for activating and deactivating the apparatus 2 and/or pulse determination process.
• the user interface components can also or alternatively comprise a display, or other visual indicator (such as a light) for providing information to the subject about the operation of the apparatus 2.
• the user interface components can comprise an audio source for providing audible feedback to the subject about the operation of the apparatus 2.
• the flow chart in Figure 3 illustrates a method of determining a pulse signal from a video sequence according to an embodiment.
• an imaging unit 4 obtains a video sequence.
• the video sequence is made up of a series of image frames.
• a series of image frames 20 is shown in Figure 4(a).
• Each video sub-sequence comprises a frame segment from each of two or more image frames 20 in a subset of image frames 20 in the video sequence.
• Each video sub-sequence is preferably formed from a frame segment from each of two or more consecutive image frames 20 (and thus each subset of image frames 20 preferably comprises consecutive image frames 20).
• Figure 4(a) illustrates four consecutive image frames 20 from a video sequence, which are labelled F1, F2, F3, F4 respectively.
• Subsets 21 of image frames are formed that, in this example, each comprise three consecutive image frames 20.
• the subsets 21 are formed such that the subsets 21 are overlapping (i.e. it is possible for some or all of the image frames 20 to be part of more than one subset 21).
• Figure 4(a) shows a first subset 21, subset 1, that comprises image frames F1, F2 and F3, a second subset 21, subset 2, that comprises image frames F2, F3 and F4 and a third subset 21, subset 3, that comprises image frames F3, F4 and F5, etc.
• Each subset 21 therefore overlaps by two image frames 20 with the preceding adjacent subset 21.
• the subsets 21 can comprise a different number of image frames 20, and/or the extent of overlap between the subsets 21 can be different.
• the subsets 21 can comprise three image frames 20, but each subset 21 may only overlap with the preceding adjacent subset 21 by one image frame 20.
• there may be no overlap between the image frames 20 in the subsets 21 i.e. in this case each image frame 20 will only be part of a single subset 21).
• Each image frame 20 is divided into a plurality of frame segments 22.
• Each frame segment 22 is a group of neighboring pixels in the image frame 20.
• An exemplary segmentation is illustrated in Figure 4(b) in which each frame 20 is divided into equal-sized squares.
• the segments can be a different shape, such as triangular.
• the shape of the segments 22 can be determined by the image in the image frame 20 (for example the boundaries of the shapes can follow boundaries between different colors in the image frame). In each embodiment, however, it will be appreciated that each frame segment 22 comprises a group of spatially- related (i.e. neighboring) pixels in each image frame 20.
• the frame segments 22 are also known in the art as‘super pixels’, for example as described in “SLIC Superpixels Compared to State-of-the-art Superpixel Methods” by Achanta et al., IEEE Transactions on Pattern Analysis & Machine Intelligence 2012 vol.34 Issue No.11, Nov.2012, pp: 2274-2282.
• each image frame 20 is divided into eighteen frame segments 22, and, for ease of reference, each frame segment 22 is given a respective label: S1,..., S18.
• the frame segments 22 can be determined by grouping pixels in the image frame 20 based on color and spatial similarities of the pixels. In this way, neighboring or closely neighboring pixels having a similar or consistent color will be grouped together into a single frame segment 22.
• an image frame 20 is divided into frame segments 22 based solely on an analysis of that image frame 20.
• the spatial and/or color based segmentation of an image frame 20 described above is extended into the time domain so that pixels sharing appearance (e.g. color) and spatial similarities in the temporal domain are grouped together. This extension into the time domain can be used to result in the same frame segmentation pattern being applied to each image frame in the subset 21, or to each image frame in the subset 21 having a respective segment pattern.
• video sub-sequences are formed from a frame segment from each of the image frames 20 in a subset. These video sub-sequences are also referred to herein as‘voxels’.
• a plurality of video sub-sequences are formed from a frame segment 22 from each of the image frames 20 in subset 1
• a plurality of video sub-sequences are formed from a frame segment 22 from each of the image frames 20 in subset 2, and so on.
• each frame segment 22 in each image frame 20 is part of a respective video sub-sequence 23, although in other embodiments this does not have to be the case (i.e. some frame segments 22 are not part of any video sub-sequences 23).
• each video sub- sequence 23 is formed using frame segments 22 in the same spatial position within each image frame 20 in the subset 21.
• one video sub-sequence 23 can be formed from the frame segment S1 in the top left corner of the image frames 20 in subset 1, another from the frame segment S13 in the bottom left corner of the image frames 20 in subset 1, and so on.
• further video sub-sequences 23 are formed using frame segments 22 in the image frames 20 in subset 2.
• Figure 4(c) illustrates the sub-sequences 23 formed from the first and second subsets 21 according to this embodiment.
• a video sub-sequence 23 can be formed by selecting frame segments 22 from the image frames 20 in a subset 21 that are consistent with each other (for example generally consistent in spatial location within the image frames 20 and generally consistent in color).
• the video sub-sequence ‘winding’ its way through the image frames 20 so that the video sub-sequence 23 contains frame segments 22 for a particular part of a subject (for example as a subject moves from left to right in the video sequence, a particular video sub-sequence 23 can be formed by a frame segment 22 in each image frame 20 that corresponds to the subject’s cheek due to spatial and color similarities).
• One preferred way to form the video sub-sequences 23 is, for a particular frame segment 22 in an image frame 20, to identify the frame segment 22 in the next image frame 20 that has the minimum chromatic energy (i.e. minimum difference in chrominance) and spatial- distance energy (i.e. minimum spatial-distance) from the particular frame segment 22.
• chromatic energy refers to an energy function based on the chrominance values of the pixels in the frame segment 22 and a frame segment 22 in the next image frame 20, and thus minimizing the chromatic energy to form a video sub-sequence 23 can comprise, for a particular frame segment 22, identifying the frame segment 22 in the next image frame 20 that has the smallest chromatic energy to the frame segment 22 under consideration. It will be appreciated that the more different the chrominance for the pixels in a frame segment 22 compared to the chrominance for the pixels in the frame segment 22 under consideration, the higher the chromatic energy, and thus the lower the likelihood that the frame segment 22 will be selected for that video sub-sequence 23.
• spatial- distance energy refers to an energy function based on the spatial-position of the frame segment 22 in the image frame 20 and the spatial-position of the frame segment 22 in the next image frame 20, and thus minimizing the spatial-distance energy to form a video sub- sequence 23 can comprise, for a particular frame segment 22, identifying the frame segment 22 in the next image frame 20 that provides the smallest spatial distance in the frame segment 22 under consideration. It will be appreciated that the larger the distance from the position of a frame segment 22 in the next image frame 20 to the position of the frame segment 22 under consideration, the higher the spatial-distance energy, and the lower the likelihood that the frame segment 22 will be selected for that video sub-sequence 23.
• the spatial-distance energy can be considered after projecting RGB pixel values to a subspace (e.g. UV).
• a subspace e.g. UV
• the division of the image frames 20 into the plurality of frame segments 22 described above provides video sub-sequences at a first scale or resolution, with the scale or resolution being indicated by the number of frame segments 22 in each image frame (e.g. eighteen in Figure 4), or by the size of each frame segment 22 (e.g. in terms of the number of image pixels per frame segment 22).
• the image frames 20 can also be divided into a second plurality of frame segments 22 having a different size/resolution to the first plurality, with a further plurality of video sub-sequences 23 being formed from those segments 22.
• This use of multiple resolutions has the advantage that it enables scale-invariant detection of a subject in a video sequence and it improves the detection of multiple subjects in a video sequence.
• the method proceeds to identify video sub-sequences 23 that can be concatenated or linked together in order to allow a pulse signal to be determined.
• the method makes use of the fact that a pulse signal in living skin tissue of a particular subject is more or less identical at different places of the skin (provided that normalized color values are considered), which means that video sub-sequences 23 from (slightly) different spatial parts of the video sequence can be concatenated together.
• a first video sub-sequence 23 formed from frame segments 22 from a first subset 21 of image frames 20 is compared to video sub-sequences 23 formed from frame segments from a second subset 21 of image frames 20.
• the second subset 21 is preferably the subset adjacent the first subset 21.
• the comparison preferably comprises comparing at least one representative value for the first video sub-sequence 23 to respective representative values for video sub- sequences 23 in the second subset 21.
• a representative value for a video sub-sequence is based on or derived from the content (e.g. pixel values) of the frame segments 22 that make up the video sub-sequence 23.
• each video sub-sequence 23 can have more than one representative value.
• step 105 can comprise comparing multiple ones of the representative values for a particular video sub-sequence 23 to multiple ones of the representative values for other video sub-sequences 23.
• Figure 4(d) illustrates an example of step 105.
• a first video sub-sequence 24 formed from frame segments 22 in image frames 20 in a first subset 21 (subset 1 comprising image frames F1, F2 and F3), and in particular a sub-sequence 24 formed from segment S9 in image frames F1, F2 and F3, is compared to video sub-sequences 25 formed from frame segments 22 in image frames 20 in a second subset 21 (subset 2 comprising image frames F2, F3 and F4).
• the first video sub-sequence 24 can be compared to all of the video sub-sequences 25 formed from the second subset 21 of image frames 20.
• the first video sub-sequence 24 can be compared to only some of the video sub-sequences 25 formed from the second subset 21 of image frames 20.
• the first video sub-sequence 24 may only be compared to the video sub- sequences 25 that are in the same and/or similar spatial positions in the image frames 20. This is illustrated in Figure 4(d) by the first video sub-sequence 24 only being compared with the labelled video sub-sequences 25 in frames F2, F3 and F4, i.e. the video sub-sequences 25 comprising segments S2, S3, S4, S8, S9, S10, S14, S15 and S16.
• the comparison in step 105 aims to identify a video sub-sequence 25 that the first video sub-sequence 24 can be concatenated with, and as such, the comparison aims to identify a or the video sub-sequence 25 that has a representative value that is similar or most similar to the representative value of the first video sub-sequence 24.
• the video sub-sequence 25 should also be in a similar spatial position in the image frames 20 to the first video sub-sequence 24 (e.g. as illustrated in Figure 4(d)).
• the representative values for each video sub- sequence 23 can be determined.
• the representative value for a particular video sub-sequence 23 formed from frame segment SX in image frames Y to Z in a video sequence is denoted V SX:Y ⁇ Z .
• the annotation used for the representative value V for each video sub- sequence 23 in Figure 4(c) is shown beneath the video sub-sequences themselves.
• a representative value for each video sub-sequence 23 can be determined from an average of the pixel values (e.g. RGB values) of pixels in all of the frame segments 22 in the video sub-sequence 23.
• each value can be the average of the pixel values of pixels in a respective frame segment 22 in the video sub- sequence 23.
• the pixel values when determining the average, can be weighted based on spatial position of the pixel in the frame segment 22 and/or a difference in color with a pixel or group of pixels at or near the center of the frame segment 22, and the average of the weighted values determined.
• the pixel values can be weighted based on the distance from the pixel to the spatial boundary, and/or the distance from the pixel to the center of the frame segment 22.
• the weighting leads to the pixels close to the segment 22 boundary being less weighted as they are less reliable than pixels close to the middle of the segment 22 due to jittering artefacts between neighboring segments 22.
• the average of the pixel values determined above is normalized, and the normalized average is used as the representative value.
• the average can be normalized by dividing the average by an average (or weighted average, if desired) of the pixel values of all the pixels in all of the frame segments 22 in the video sub-sequence 23.
• the average can be normalized by dividing the average by an average (or weighted average, if desired) of the pixel values of all of the pixels in all of the image frames 20 in the subset 21 from which the video sub-sequence 23 is formed.
• a representative value for each video sub- sequence 23 can be determined from a difference between the averages (or weighted averages) of the pixel values of pixels for each frame segment in the video sub-sequence 23.
• the differences can be normalized using the average (or weighted average) of pixel values across all frame segments 22 in the sub-sequence 23.
• the differences can be normalized using a sum of the average (or weighted average) of pixel values across all frame segments 22 in the sub-sequence 23. The use of normalized differences is advantageous as it allows the difference values (i.e.
• the concatenated difference values can be integrated before or after a pulse signal is derived (step 109 below).
• step 107 based on the comparison between the first video sub-sequence 24 and the video sub-sequences 25 from the second subset 21, the first video sub-sequence 24 is concatenated with a video sub-sequence 25 from the second subset 21 that is sufficiently similar (e.g. the representative values are within a particular amount of each other) or most similar to the first video sub-sequence 24 (this video sub-sequence 25 is referred to herein as the second video sub-sequence 25).
• steps 105 and 107 can be repeated for the second video sub-sequence 25 to identify a third video sub-sequence 26 that is formed from frame segments from the third subset 21 of image frames 20 (i.e. F3, F4, F5).
• the third video sub-sequence 26 can be concatenated with the second video sub-sequence 25 as shown in Figure 4(e).
• Steps 105 and 107 can then be repeated to identify video sub-sequences from further subsets 21 of image frames 20 that are to be concatenated with the sub-sequences 24, 25, 26.
• a pulse signal is determined from the video sub-sequences (step 109).
• the pulse signal represents the color, or changes in the color, of the frame segments 22 and/or video sub- sequences 23 in the concatenated sequence.
• step 109 can comprise determining the pulse signal from the representative values of each of the concatenated video sub-sequences 24, 25, 26.
• the pulse signal can be formed from the representative values themselves, for example as shown in Figure 4(f).
• V S9:1 ⁇ 3 , V S10:2 ⁇ 4 and V S...:3 ⁇ 5 provide the values of the pulse signal 27 at times t F1 , t F2 , t F3 respectively (corresponding to the timing of the first image frame represented in each video sub-sequence 24, 25, 26).
• the video sub-sequences 24, 25, 26 forming the pulse signal 27 contain an area of living skin tissue, and therefore the pulse signal 27 determined from this concatenated set of video sub-sequences will exhibit characteristics typical of a PPG signal (i.e. varying in amplitude consistent with changes in blood perfusion in the skin of the subject due to the beating of the heart). If the concatenated set of video sub-sequences did not contain an area of living skin tissue, the pulse signal determined from this set will not exhibit characteristics that are typical of a PPG signal (and, in the absence of changes in the ambient lighting in the video sequence, the pulse signal for this set may correspond generally to a noise signal).
• steps 107 and 109 can effectively be viewed as one step, as the or a representative value for the first video sub- sequence 24 is concatenated with the or a representative value for the second video sub- sequence 25.
• the concatenated video sub-sequences 24, 25, can be analyzed using techniques for extracting a pulse signal from a sequence of images/image frames/frame segments.
• techniques for determining a pulse signal from a video sequence are known in the art and will not be described in detail herein. However, some exemplary techniques are mentioned in the description of the Voxel-Pulse-Spectral method presented below.
• the method can return to step 105 and repeat for a different one of the video sub-sequences 23 that is formed from the image frames 20 in the first subset, subset 1.
• a plurality of pulse signals 27 will be determined for different (spatial) parts of the video sequence, some of which may include areas of living skin tissue, whereas others may not. It will be appreciated that due to the way in which video sub- sequences 23 from different subsets 21 are compared to each other (i.e. by a comparison of representative values), it is possible that a particular video sub-sequence 23 can contribute to several different pulse signals 27 (or indeed a particular video sub-sequence 23 may not contribute to any pulse signals 27).
• the pulse signal 27 can be analyzed to identify areas of living skin tissue in the video sequence.
• the analyzing can comprise analyzing the pulse signals obtained in different resolutions together to identify the areas of living skin tissue.
• the pulse signals 27 can be clustered together based on similarities (e.g. spatial, temporal, color and/or frequency similarities), and the areas of living skin tissue identified from those clusters.
• the pulse signal 27 or pulse signals 27 can be analyzed to determine which, if any, exhibit characteristics of living skin tissue.
• frequency characteristics of the pulse signal(s) is/are determined, and the determined frequency characteristics of the pulse signal(s) is/are compared to typical frequency characteristics of pulse signals obtained from areas of living skin tissue.
• a fixed frequency threshold or band e.g. corresponding to a typical heart beat/pulse frequency
• the pulse signals 27 are spatially clustered based on similarities in the pulse signals.
• a suitable clustering algorithm for implementing this is density-based spatial clustering of applications with noise (DBSCAN), or the clustering proposed in“Face detection method based on photoplethysmography” that is referenced above.
• pairwise similarities for each pulse signal 27 with the other pulse signals 27 can be determined. That is, for each pulse signal, a measure of the similarity with each of the other pulse signals is determined. These pairwise similarity measures are then analyzed to identify areas of living skin tissue.
• the pairwise similarity measures preferably include or are frequency-based pairwise similarity measures. This is advantageous since different frame segments 22 corresponding to areas of living skin tissue of the same particular subject should exhibit pulse signals that have similar (or the same) frequency peak index, phase or a low entropy in the correlation.
• the measure of pairwise similarity for a pulse signal and one of the other pulse signals can be a measure of the correlation between at least part of the frequency spectra of the pulse signal and the one of the other pulse signals (which is referred to herein as the spectrum peak), a measure of the normalized cross-correlation between at least part of the frequency spectra of the pulse signal and the one of the other pulse signals (which is referred to herein as the spectrum phase), a measure of the regularity of correlation between at least part of the frequency spectra of the two pulse signals (which is referred to herein as the spectrum entropy) and/or the result of an inner product of the two pulse signals (which can optionally be filtered before the inner product is calculated). Further details of these pairwise similarity measures can be found in the description of the Voxel-Pulse-Spectral (VPS) method below.
• VPS Voxel-Pulse-Spectral
• multiple measures of pairwise similarity can be determined for each pulse signal with each of the other pulse signals, and those measures combined to form a distance metric representing the pairwise similarity for each pulse signal with each of the other pulse signals.
• a similarity matrix for the video sequence can be determined, and areas of living skin tissue can be identified from the similarity matrix.
• the similarity matrix can be formed by combining the pairwise similarities (or distance metrics) determined for the pulse signals.
• the similarity matrix is a matrix in which similar pulse signals across the two or more pluralities of video sub-sequences are mutually correlated.
• the use of a similarity matrix is advantageous as it does not require any parameters to be predefined (e.g. parameters based on skin tone or clustering).
• areas of living skin tissue are identified by performing matrix decomposition of the similarity matrix.
• the matrix decomposition can by singular value decomposition (SVD), QR decomposition, sparse SVD, incremental SVD, principal component analysis, PCA, or independent component analysis, ICA. These techniques are generally known in the art and will not be described in detail herein. In a preferred embodiment, which is described in more detail below, the similarity matrix is decomposed using incremental sparse PCA.
• SVD singular value decomposition
• QR decomposition sparse SVD
• incremental SVD principal component analysis
• PCA principal component analysis
• ICA independent component analysis
• the decomposition can comprise factorizing (decomposing) the similarity matrix into orthogonal bases to find the parts of the video sequence belonging to the same subject. This factorizing results in different kinds of similarities being separated into independent directions. This results in the frame segments belonging to the same subject being clustered in the same direction.
• one or more physiological characteristics of the subject can be determined from the video sequence.
• the physiological characteristic(s) can be determined from the one or more pulse signals 27 associated with the identified areas of living skin tissue.
• the one or more pulse signals 27 can be individually analyzed to determine a physiological characteristic and the physiological characteristics combined (e.g. averaged) to give an overall measure of the physiological characteristic for the subject.
• the one or more pulse signals 27 can be combined (e.g. averaged) to give a single pulse signal, and the pulse signal analyzed to determine a physiological characteristic.
• the video sequence can be re-processed to extract a pulse signal or signals from the areas identified to be living skin tissue, and that pulse signal(s) processed to determine the physiological characteristic.
• the pulse signal(s) derived from the video sequence are similar to signals obtained using a PPG sensor, so the one or more physiological characteristics of the subject can include any characteristic that can be derived from a PPG signal or other measure of the blood perfusion (or changes in the blood perfusion) of the subject, such as heart rate, heart rate variability, beat-to-beat interval, breathing rate, breathing signal, SpO2 value (i.e. the arterial oxygenation level of the blood), etc.
• a characteristic that can be derived from a PPG signal or other measure of the blood perfusion (or changes in the blood perfusion) of the subject such as heart rate, heart rate variability, beat-to-beat interval, breathing rate, breathing signal, SpO2 value (i.e. the arterial oxygenation level of the blood), etc.
• PPG signals e.g. peak detection in the frequency domain for determining heart rate
• Voxel-Pulse-Spectral (VPS) method A particular embodiment of the techniques presented herein is described below, and is referred to herein as the Voxel-Pulse-Spectral (VPS) method.
• Voxel-Pulse-Spectral (VPS) method Camera-based pulse extraction
• rPPG remote photoplethysmography
• blind Source Separation methods e.g. PCA-based and ICA-based
• a Chrominance-based rPPG method has also been proposed to define the pulse as a linear combination of RGB channels under a standardized skin-tone assumption, which is one of the most accurate rPPG methods in dealing with realistic challenges (e.g. different subject skin colors). Pulse-based region of interest detection
• the pulse signal can thus be used to assist the subject detection, i.e. detecting alive subjects by locating their living skin tissue.
• An existing technique proposes a face detection method based on the pulse signal which slices the video into fixed rigid-grids for local pulse extraction. It sets a hard threshold to find the grids with a high spectrum energy and label them as the face region. It is limited to videos in which the stationary face needs to be placed at a pre-defined distance from the camera. The VPS method described herein does not suffer from these limitations.
• an optimal Region of Interest (RoI) selection method on the face to enhance the rPPG monitoring was proposed.
• the RoI is constrained to predefined facial landmarks, which is not a general solution for subject detection, i.e. it cannot detect other body parts (e.g. hands) that might be visible in a video.
• the VPS method described herein does not make such an assumption and can detect all body parts with pulsatile blood volume.
• FIG. 5 An overview of the VPS method is shown in Figure 5, which takes an input video sequence comprising image frames 20 and outputs the subject RoI 52 (i.e. the areas of the video sequence that correspond to living skin tissue). Consistent with the method shown in Figure 3, the video sequence is segmented into a plurality of sub-sequences (hierarchical voxels stage 54– step 103), pulse signals are determined from the sub-sequences (pulse extraction stage 56– steps 105-109), a similarity matrix is determined and analyzed to identify RoIs (spectral analysis stage 58). Each stage is discussed in detail below. Hierarchical voxels
• the video sequence is first segmented into dense local regions where a pulse can be independently measured (this is the hierarchical voxels stage 54 in Figure 5).
• the video sequence can be sliced into fixed rigid-grids, this means that the subject size is quantized by the grid geometry, which struggles or fails when the subject is small or when there is body motion. Therefore, in the VPS method, it is preferred to use a superior video segmentation method for pulse extraction which is called‘hierarchical voxels’.
• the hierarchical voxels consist of spatiotemporally coherent clusters (frame segments), in multiple scales (with the scale determining the number of clusters/segments that each image frame 20 is divided into), where pixels in image frames 20 sharing appearance and spatial similarities in the temporal domain are grouped together.
• multiple scales are preferred and used in the following description of the VPS method, it is possible to use a single scale in the VPS method.
• constructing the voxels is defined as the procedure of minimizing the chromatic energy E c (i.e. minmizing the difference in chrominance for the frame segments) and spatial-distance energy E s (i.e.
• the representation of p is a 4- dimensional feature vector (x, y, u, v), where (x, y) and (u, v) are respectively the coordinates in the image plane and the chromatic plane (e.g. UV plane of YUV space, the empirical space for skin segmentation).
• K-means clustering is performed to assign pixels into k clusters for minimizing the total energy during T.
• ⁇ is the parameter controlling the balance between two energy terms.
• the single scale voxels are extended to multiple scales by initializing different k in equation 1 simultaneously, where each scale is an independent clustering.
• the ⁇ i in i-th scale is adaptively self-tuned based on its own energy variations at t as: where ⁇ ( ⁇ ) denotes the standard deviation operator; ⁇ ( ⁇ ) represents the set of cluster means; log(k) controls the voxel compactness, i.e. voxels with higher resolution (larger k) should be more compact.
• the real-time tuning of ⁇ in different scales avoids volatile and flickering clustering, which preserves the fine-grained segmentation.
• the first image 60 corresponds to the left hand image in Figure 1 and shows a human face and an artificial face that are both facing the camera.
• the second image 62 corresponds to the right hand image in Figure 1 and shows a human face and artificial face side on to the camera.
• Each voxel (i.e. video sub-sequence) in the hierarchy is assumed to be an independent pulse sensor in parallel pulse extraction.
• the Chrominance-based method (CHROM) is used for pulse measurement, which is described in“Robust pulse rate from chrominance- based rPPG” by G. de Haan and V. Jeanne, TBME, 60(1):2878-2886, 2013.
• CHROM Chrominance-based method
• N denotes the number of pixels in j-th voxel.
• human skin tissue shows the same relative PPG-amplitude, but the chromatic differences in voxels lead to the variations in pulse-amplitudes. So different from CHROM, the temporal derivatives of average RGB in a voxel are used, i.e.
• ⁇ ( ⁇ ) denotes the averaging operation. Note that the pulse-signal is the only feature used in this method. No other appearance features like color or texture are used. Spectral analysis
• This section describes spectral analysis stage 58, which comprises three sub- stages, forming a similarity matrix, performing incremental sparse PCA on the similarity matrix, and using hierarchical fusion to identify areas of living skin tissue in the video sequence.
• pulse signals extracted from skin regions belonging to the same subject share similarities in many aspects such as phase and frequency, whereas the ones extracted from non-skin regions (e.g. background) are random noises without correlation. Therefore, after extracting the pulse signals from hierarchical voxels, pairwise similarities of pulse signals are used to find alive subjects. This is also applicable to the case of multiple subjects in the video sequence, because the pulse measured from different subjects can be differentiated in phase and frequency as well.
• Similarity matrix (D,C) is created to interconnect the hierarchical voxels based on the measured pulse.
• the entries D in the diagonal trace contain all voxels in different scales; the remaining entries C denote the pairwise connection between any pair of voxels.
• the distance metric for measuring the pulse similarity needs to be defined. The most commonly used distance metrics, i.e. L1 and L2 distances, are not applicable to the pulse feature.
• pulse signals from the same subject show the following relations: (1) they have similar frequency and thus their cross-correlation presents a significant spectrum peak; (2) they have no significant phase shift; (3) their frequency correlation is regular and less disordered; and (4) if considering pulse signals as multidimensional vectors, the included angle between two similar vectors is small. Therefore, a preferred distance metric used to build the similarity matrix for pulse signals emphasizes the above connections, and is composed of four different measurements:
• a pulse-rate band BPM Beats Per Minute
• the spectrum peak of two cross-correlated pulse-signals is defined as:
• Figure 7 shows an example of four measurements and their fused similarity matrix ⁇ 64, 66 for two video sequences, one that includes a single living subject, and the other including two living subjects.
• the entries with higher energy represent the index of similar voxels in the hierarchy.
• two well-aligned pulse signals show boosted frequency energy during the cross correlation, which can effectively suppress the noise entries (e.g. voxels without pulse).
• previous distance metrics are all objective measurements that cannot enhance the connection between similar entries in the comparison.
• all voxels in the hierarchy are mutually connected in the similarity matrix. The task of detecting an alive subject in voxels can be reformulated as finding a subspace partition of the similarity matrix such that the entries in the same subspace have identical similarity direction.
• Incremental sparse matrix decomposition The similarity matrix ⁇ 64, 66 can be interpreted as a linear combination of where is a set of orthogonal vectors in the multi-dimensional space.
• a matrix decomposition technique is used to factorize ⁇ into X, where different subjects are separated into different eigenvectors. Since ⁇ is a sparse matrix with many zero entries (e.g. the voxels pointing at background share no similarity), sparse PCA is applied to decompose ⁇ into X by seeking a trade-off between expressive power and data interpretability.
• Sparse PCA is described in“Sparse PCA: Convex relaxations, algorithms and applications” by Y. Zhang, A. d’Aspremont and L. Ghaoui, International Series in Operations Research & Management Science Volume 166:915-940, 2012.
• the Sparse PCA finds the first sparse eigenvector with the maximum variance in ⁇ by optimizing the following non- convex objective function:
• HCGS Hybrid Conditional Gradient Smoothing
• ⁇ may consist of multiple sparse eigenbasis in case of multiple subjects, whereas equation 14 only promotes the sparsity in the first leading eigenvector.
• the succeeding sparse eigenvectors x i are estimated by sequentially deflating ⁇ with preceding sparse eigenvectors using Hotelling’s
• m is the automatically found number of most expressive eigenvectors, which also implies the number of subjects in a video sequence, i.e. m is usually found at the largest eigenvalue gap.
• Figure 8 shows an example of similarity matrix decomposition using incremental sparse PCA for the two similarity matrices 64, 66 shown in Figure 7, where similar voxels are factorized into the same directions in the selected eigenvectors.
• Figure 8 shows the factorized and selected eigenbasis from a similarity matrix; the noisy entries in the original ⁇ 64, 66 are eliminated in 68, 70; the eigenvalues (shown in graphs 72 and 74 respectively) clearly show the number of most expressive eigenvectors in
• Hierarchical fusion By projecting the estimated sparse eigenvectors 76, 78 onto the hierarchical voxels, a voxel-based human objectness map in multiple scales is obtained, where each scale has a different quantized description to the subject.
• This projection for the video sequence containing two subjects from Figures 7 and 8 is illustrated in Figure 9.
• the eigenvector 78 not only decides the subject direction (sign) in subspaces, but also decides the pulsatility (amplitude) of corresponding skin-regions, i.e., forehead and cheek show relatively high pulsatility in projection.
• the final step is to fuse multiple objectness maps into a single output.
• the above method, and the preferred embodiment of the VPS method provide an improved method and apparatus for identifying living skin tissue in a video sequence.
• the method provides improved living skin tissue detection rates compared to conventional techniques, with the detection being based solely on using pulse signals to detect living tissue. These improvements are obtained regardless of the scale (i.e. distance from the imaging unit 4), posture, position, skin tone, visible body part or motion of the subject, or background of the subject in the video sequence, whether the subject is partially occluded from the imaging unit 4, whether there is an artificial face or body part present in the video sequence, or whether there are multiple living subjects in the video sequence.

Landscapes

• Engineering & Computer Science (AREA)
• Health & Medical Sciences (AREA)
• Physics & Mathematics (AREA)
• Theoretical Computer Science (AREA)
• Life Sciences & Earth Sciences (AREA)
• General Physics & Mathematics (AREA)
• General Health & Medical Sciences (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Multimedia (AREA)
• Medical Informatics (AREA)
• Biophysics (AREA)
• Cardiology (AREA)
• Heart & Thoracic Surgery (AREA)
• Artificial Intelligence (AREA)
• Evolutionary Computation (AREA)
• Human Computer Interaction (AREA)
• Animal Behavior & Ethology (AREA)
• Surgery (AREA)
• Molecular Biology (AREA)
• Public Health (AREA)
• Veterinary Medicine (AREA)
• Biomedical Technology (AREA)
• Pathology (AREA)
• Data Mining & Analysis (AREA)
• Software Systems (AREA)
• Physiology (AREA)
• Databases & Information Systems (AREA)
• Computing Systems (AREA)
• Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
• Radiology & Medical Imaging (AREA)
• Quality & Reliability (AREA)
• Bioinformatics & Cheminformatics (AREA)
• Bioinformatics & Computational Biology (AREA)
• Evolutionary Biology (AREA)
• General Engineering & Computer Science (AREA)
• Oral & Maxillofacial Surgery (AREA)
• Probability & Statistics with Applications (AREA)
• Image Analysis (AREA)
• Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)

Priority Applications (3)

Applications Claiming Priority (2)

Publications (1)

Family

ID=53268656

Family Applications (1)

Country Status (5)

Cited By (3)

Families Citing this family (33)

Citations (1)

Family Cites Families (14)

• 2016
  • 2016-05-09 JP JP2017560151A patent/JP6665203B2/ja not_active Expired - Fee Related
  • 2016-05-09 EP EP16723310.5A patent/EP3298536A1/en not_active Withdrawn
  • 2016-05-09 CN CN201680029350.8A patent/CN107666853A/zh active Pending
  • 2016-05-09 WO PCT/EP2016/060250 patent/WO2016184705A1/en not_active Ceased
  • 2016-05-20 US US15/159,932 patent/US10290104B2/en not_active Expired - Fee Related

Patent Citations (1)

Non-Patent Citations (12)

Also Published As

Similar Documents

Legal Events