CN110929545A - Human face image sorting method and device - Google Patents
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Abstract
The disclosure relates to a method and a device for arranging face images. The method comprises the following steps: acquiring a plurality of candidate images corresponding to a target person; respectively extracting feature vectors of the face regions in the candidate images; determining similarity between feature vectors of face regions in different candidate images of the plurality of candidate images; and determining the target face image from the candidate images corresponding to the two feature vectors with the highest similarity. The face image sorting method and device can achieve automatic face image sorting, time and labor are saved, and the image quality of the target face image obtained through sorting can be guaranteed.
Description
Technical Field
The present disclosure relates to the field of image technologies, and in particular, to a method and an apparatus for collating face images.
Background
In the related art, the face images are collated manually. For example, a plurality of photos of the same star are collected from the web, and then the photos of the same star are collated by means of manual screening. The arrangement mode of the face images wastes time and labor, and the quality of the face images obtained by arrangement cannot be guaranteed. For the face images of unknown stars, the condition of 'blind face' may appear in the arrangement, and the arrangement difficulty is high.
Disclosure of Invention
In view of this, the present disclosure provides a method and an apparatus for collating face images.
According to an aspect of the present disclosure, there is provided a method for collating face images, including:
acquiring a plurality of candidate images corresponding to a target person;
respectively extracting feature vectors of the face regions in the candidate images;
determining similarity between feature vectors of face regions in different candidate images of the plurality of candidate images;
and determining the target face image from the candidate images corresponding to the two feature vectors with the highest similarity.
In one possible implementation manner, acquiring a plurality of candidate images corresponding to a target person includes:
acquiring a plurality of images to be sorted corresponding to a target person;
and determining a candidate image corresponding to the target person according to the image to be sorted meeting a first condition in the plurality of images to be sorted.
In one possible implementation, the first condition includes: the number of the face regions in the image to be sorted is 1.
In one possible implementation, the first condition further includes one or more of:
the resolution ratio of the face area in the image to be sorted is greater than a first threshold value;
the face angle in the image to be sorted belongs to a first interval;
and the position of the face area in the image to be sorted belongs to a second interval.
In a possible implementation manner, determining a target face image from candidate images corresponding to two feature vectors with the highest similarity includes:
and randomly selecting one of the candidate images corresponding to the two feature vectors with the highest similarity as a target face image.
In a possible implementation manner, determining a target face image from candidate images corresponding to two feature vectors with the highest similarity includes:
determining a face quality score of a candidate image, wherein the candidate image represents a candidate image corresponding to two feature vectors with the highest similarity;
and determining the alternative image with higher face quality score as the target face image.
In one possible implementation, determining a face quality score of the candidate image includes:
and determining the face quality score of the alternative image according to one or more of the resolution of the face region in the alternative image, the face angle in the alternative image and the position of the face region in the alternative image.
In one possible implementation manner, after determining the target face image from the candidate images corresponding to the two feature vectors with the highest similarity, the method further includes:
and intercepting a face area from the target face image to obtain a face area image of the target person.
In one possible implementation manner, after obtaining the face area image of the target person, the method further includes:
and saving the face region image.
In one possible implementation manner, after obtaining the face area image of the target person, the method further includes:
and establishing face index data according to the feature vector of the face region image.
According to another aspect of the present disclosure, there is provided a face image collating device, including:
the acquisition module is used for acquiring a plurality of candidate images corresponding to the target person;
the extraction module is used for respectively extracting the characteristic vectors of the face regions in the candidate images;
a first determining module, configured to determine similarity between feature vectors of face regions in different candidate images of the plurality of candidate images;
and the second determining module is used for determining the target face image from the candidate images corresponding to the two feature vectors with the highest similarity.
In one possible implementation manner, the obtaining module includes:
the acquisition submodule is used for acquiring a plurality of images to be sorted corresponding to the target person;
and the first determining submodule is used for determining a candidate image corresponding to the target person according to the image to be sorted meeting a first condition in the plurality of images to be sorted.
In one possible implementation, the first condition includes: the number of the face regions in the image to be sorted is 1.
In one possible implementation, the first condition further includes one or more of:
the resolution ratio of the face area in the image to be sorted is greater than a first threshold value;
the face angle in the image to be sorted belongs to a first interval;
and the position of the face area in the image to be sorted belongs to a second interval.
In one possible implementation manner, the second determining module is configured to:
and randomly selecting one of the candidate images corresponding to the two feature vectors with the highest similarity as a target face image.
In one possible implementation manner, the second determining module includes:
the second determining submodule is used for determining the face quality scores of the alternative images, wherein the alternative images represent candidate images corresponding to two feature vectors with the highest similarity;
and the third determining submodule is used for determining the alternative image with higher face quality score as the target face image.
In one possible implementation, the second determining submodule is configured to:
and determining the face quality score of the alternative image according to one or more of the resolution of the face region in the alternative image, the face angle in the alternative image and the position of the face region in the alternative image.
In one possible implementation, the apparatus further includes:
and the intercepting module is used for intercepting a face area from the target face image to obtain a face area image of the target person.
In one possible implementation, the apparatus further includes:
and the storage module is used for storing the face region image.
In one possible implementation, the apparatus further includes:
and the establishing module is used for establishing face index data according to the characteristic vector of the face region image.
According to another aspect of the present disclosure, there is provided a face image collating device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the above method.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the above-described method.
In the embodiment of the disclosure, a plurality of candidate images corresponding to a target person are obtained, feature vectors of face regions in the candidate images are respectively extracted, the similarity between the feature vectors of the face regions in different candidate images in the candidate images is determined, and the target face image is determined from the candidate images corresponding to two feature vectors with the highest similarity, so that the face images can be automatically sorted, time and labor are saved, and the image quality of the sorted target face images can be guaranteed.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.
Fig. 1 shows a flowchart of a method for collating face images according to an embodiment of the present disclosure.
Fig. 2 shows an exemplary flowchart of step S11 of the method for collating face images according to an embodiment of the present disclosure.
Fig. 3 shows an exemplary flowchart of step S14 of the method for collating face images according to an embodiment of the present disclosure.
Fig. 4 shows an exemplary flowchart of a face image sorting method according to an embodiment of the present disclosure.
Fig. 5 shows another exemplary flowchart of a face image sorting method according to an embodiment of the present disclosure.
Fig. 6 shows a block diagram of a face image collating device according to an embodiment of the present disclosure.
Fig. 7 shows an exemplary block diagram of a face image collating device according to an embodiment of the present disclosure.
Fig. 8 is a block diagram illustrating an apparatus 800 for collating face images according to an exemplary embodiment.
Fig. 9 is a block diagram illustrating an apparatus 1900 for collating face images according to an exemplary embodiment.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
Fig. 1 shows a flowchart of a method for collating face images according to an embodiment of the present disclosure. The execution subject of the human face image arrangement method can be a human face image arrangement device. For example, the method for collating the face image may be executed by a terminal device or a server or other processing device. The terminal device may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, or a wearable device. In some possible implementations, the method for collating face images may be implemented by a processor calling computer readable instructions stored in a memory. As shown in fig. 1, the method includes steps S11 through S14.
In step S11, a plurality of candidate images corresponding to the target person are acquired.
In the embodiment of the present disclosure, the target person represents a person who needs face image collation. The number of the target persons may be one or more. In an application scene of face image arrangement, a large number of target characters may exist. For example, in the application scenario of face image collation, the target person includes thousands of star characters. In the embodiment of the present disclosure, steps S11 to S14 may be performed separately for each target person.
In the embodiment of the disclosure, the name of the target person may be used for searching in a search engine, and a plurality of candidate images corresponding to the target person may be obtained based on the search result.
For example, for each target person, 10 candidate images corresponding to the target person may be acquired.
In step S12, feature vectors of the face regions in the plurality of candidate images are extracted, respectively.
In a possible implementation manner, the extracting feature vectors of the face regions in the multiple candidate images respectively includes: and for the face region in any candidate image, extracting the characteristic points in the face region, and performing characteristic description on the characteristic points in the face region to obtain the characteristic vector of the face region. For example, the number of feature points is 128.
In step S13, a similarity between feature vectors of face regions in different candidate images of the plurality of candidate images is determined.
In the embodiment of the present disclosure, for feature vectors of face regions in a plurality of candidate images, similarity between each two feature vectors is calculated.
In step S14, the target face image is determined from the candidate images corresponding to the two feature vectors with the highest similarity.
In the embodiment of the present disclosure, candidate images corresponding to two feature vectors with the highest similarity may be respectively determined as candidate images, and one of the two candidate images may be selected as a target face image.
In a possible implementation manner, determining a target face image from candidate images corresponding to two feature vectors with the highest similarity includes: and randomly selecting one of the candidate images corresponding to the two feature vectors with the highest similarity as a target face image.
In the embodiment of the disclosure, a plurality of candidate images corresponding to a target person are obtained, feature vectors of face regions in the candidate images are respectively extracted, the similarity between the feature vectors of the face regions in different candidate images in the candidate images is determined, and the target face image is determined from the candidate images corresponding to two feature vectors with the highest similarity, so that the face images can be automatically sorted, time and labor are saved, and the image quality of the sorted target face images can be guaranteed.
Fig. 2 shows an exemplary flowchart of step S11 of the method for collating face images according to an embodiment of the present disclosure. As shown in fig. 2, step S11 may include step S111 and step S112.
In step S111, a plurality of images to be sorted corresponding to the target person are acquired.
In the embodiment of the disclosure, the name of the target person can be used for searching in a search engine, and a plurality of images to be sorted corresponding to the target person are obtained. Wherein, the image to be sorted can represent the searched image.
In step S112, a candidate image corresponding to the target person is determined according to an image to be sorted satisfying a first condition among the plurality of images to be sorted.
In one possible implementation manner, a plurality of candidate images corresponding to the target person may be selected from the images to be sorted satisfying the first condition. For example, 10 candidate images corresponding to the target person may be selected from the images to be sorted satisfying the first condition.
In one possible implementation, the first condition includes: the number of face regions in the image to be sorted is 1. In this implementation, if the number of face regions in the image to be sorted is not 1, it can be largely indicated that the image to be sorted is a group photograph of the target person and other persons. In order to avoid interference of other people except the target person in the image to be sorted on the sorting of the face image of the target person, in the implementation manner, if the number of the face areas in the image to be sorted is not 1, the image to be sorted is not taken as the candidate image corresponding to the target person.
As an example of this implementation, a Dlib library may be used to identify face regions in the images to be sorted.
In one possible implementation, the first condition further includes one or more of: the resolution ratio of the face area in the image to be sorted is larger than a first threshold value; the face angle in the image to be sorted belongs to a first interval; the position of the face region in the image to be sorted belongs to the second interval.
As one example of this implementation, the first condition includes: the number of the face regions in the image to be sorted is 1, and the resolution of the face regions in the image to be sorted is greater than a first threshold. According to the example, the resolution of the face region in the candidate image is limited, so that the resolution of the face image obtained through sorting can be guaranteed, and the quality of the face image obtained through sorting can be improved.
As another example of this implementation, the first condition includes: the number of the face regions in the image to be sorted is 1, and the face angle in the image to be sorted belongs to a first interval. For example, the first interval is [ -20 °, 20 ° ]. Wherein the face angle in the image to be sorted is 0 degrees, which indicates that the face angle in the image to be sorted is a front face; the face angle in the image to be sorted is a negative value, which can indicate that the face in the image to be sorted deflects leftwards; the angle of the face in the image to be sorted is a positive value, which can indicate that the face in the image to be sorted deflects to the right.
As another example of this implementation, the first condition includes: the number of the face regions in the image to be sorted is 1, and the positions of the face regions in the image to be sorted belong to a second interval. The position of the face region may refer to coordinates of a geometric center of the face region, and the second interval may be a designated coordinate range.
As another example of this implementation, the first condition includes: the number of the face regions in the image to be sorted is 1, the resolution of the face regions in the image to be sorted is greater than a first threshold, and the face angle in the image to be sorted belongs to a first interval.
As another example of this implementation, the first condition includes: the number of the face regions in the image to be sorted is 1, the resolution of the face regions in the image to be sorted is greater than a first threshold, and the positions of the face regions in the image to be sorted belong to a second interval.
As another example of this implementation, the first condition includes: the number of the face regions in the images to be sorted is 1, the face angles in the images to be sorted belong to a first interval, and the positions of the face regions in the images to be sorted belong to a second interval.
As another example of this implementation, the first condition includes: the number of the face regions in the image to be sorted is 1, the resolution of the face regions in the image to be sorted is greater than a first threshold, the face angle in the image to be sorted belongs to a first interval, and the position of the face region in the image to be sorted belongs to a second interval.
Fig. 3 shows an exemplary flowchart of step S14 of the method for collating face images according to an embodiment of the present disclosure. As shown in fig. 3, step S14 may include step S141 and step S142.
In step S141, a face quality score of a candidate image is determined, where the candidate image represents a candidate image corresponding to two feature vectors with the highest similarity.
In one possible implementation, determining a face quality score of the candidate image includes: and determining the face quality score of the alternative image according to one or more of the resolution of the face region in the alternative image, the face angle in the alternative image and the position of the face region in the alternative image.
As an example of this implementation, the face quality score of the candidate image is positively correlated with the resolution of the face region in the candidate image.
As one example of this implementation, the face quality score of the candidate image is inversely related to the absolute value of the face angle in the candidate image.
As one example of this implementation, the face quality score of the candidate image is negatively correlated with the first distance. Wherein the first distance is equal to the distance between the geometric center of the face region and the geometric center of the alternative image.
It should be noted that, although the manner of determining the face quality score of the candidate image is described above by taking the determination of the face quality score of the candidate image according to one or more of the resolution of the face region in the candidate image, the face angle in the candidate image, and the position of the face region in the candidate image as an example, it can be understood by those skilled in the art that the present disclosure should not be limited thereto. The implementation manner of determining the face quality score of the alternative image can be flexibly set by a person skilled in the art according to the actual application scene requirement and/or personal preference.
In step S142, the candidate image with the higher face quality score is determined as the target face image.
Fig. 4 shows an exemplary flowchart of a face image sorting method according to an embodiment of the present disclosure. As shown in fig. 4, the method may include steps S11 through S15.
In step S11, a plurality of candidate images corresponding to the target person are acquired.
In step S12, feature vectors of the face regions in the plurality of candidate images are extracted, respectively.
In step S13, a similarity between feature vectors of face regions in different candidate images of the plurality of candidate images is determined.
In step S14, the target face image is determined from the candidate images corresponding to the two feature vectors with the highest similarity.
In step S15, a face region is cut out from the target face image, and a face region image of the target person is obtained.
In a possible implementation manner, the size and the position of the cropping frame may be determined according to the size and the position of the face frame in the target face image, and the face area may be intercepted from the target face image based on the size and the position of the cropping frame to obtain the face area image of the target person.
As an example of this implementation, determining the size and position of the crop box according to the size and position of the face box in the target face image includes: and determining the size of the face frame in the target face image as the size of the cutting frame, and determining the geometric center of the face frame in the target face image as the geometric center of the cutting frame.
As another example of this implementation, determining the size and position of the crop box according to the size and position of the face box in the target face image includes: and determining the M times of the size of the face frame in the target face image as the size of the cutting frame, and determining the geometric center of the face frame in the target face image as the geometric center of the cutting frame. Wherein M is a positive number. For example, M is a positive number greater than 1. For example, M equals 1.3.
The realization method can accurately intercept the face area from the target face image.
In one possible implementation, after obtaining the face region image of the target person, the method further includes: and saving the face region image. The implementation mode can provide convenience for subsequently establishing face index data by storing the face region image.
Fig. 5 shows another exemplary flowchart of a face image sorting method according to an embodiment of the present disclosure. As shown in fig. 5, the method may include steps S11 through S16.
In step S11, a plurality of candidate images corresponding to the target person are acquired.
In step S12, feature vectors of the face regions in the plurality of candidate images are extracted, respectively.
In step S13, a similarity between feature vectors of face regions in different candidate images of the plurality of candidate images is determined.
In step S14, the target face image is determined from the candidate images corresponding to the two feature vectors with the highest similarity.
In step S15, a face region is cut out from the target face image, and a face region image of the target person is obtained.
In step S16, face index data is created based on the feature vectors of the face region images.
In a possible implementation manner, the face index data may be established by using a product quantization method according to face region images of a plurality of target persons.
Fig. 6 shows a block diagram of a face image collating device according to an embodiment of the present disclosure. As shown in fig. 6, the apparatus includes: an obtaining module 61, configured to obtain multiple candidate images corresponding to a target person; an extraction module 62, configured to extract feature vectors of face regions in the multiple candidate images, respectively; a first determining module 63, configured to determine similarity between feature vectors of face regions in different candidate images of the multiple candidate images; and a second determining module 64, configured to determine the target face image from the candidate images corresponding to the two feature vectors with the highest similarity.
Fig. 7 shows an exemplary block diagram of a face image collating device according to an embodiment of the present disclosure. As shown in fig. 7:
in one possible implementation, the obtaining module 61 includes: the obtaining sub-module 611 is configured to obtain a plurality of images to be sorted corresponding to the target person; the first determining sub-module 612 is configured to determine, according to an image to be sorted meeting a first condition in the plurality of images to be sorted, a candidate image corresponding to the target person.
In one possible implementation, the first condition includes: the number of face regions in the image to be sorted is 1.
In one possible implementation, the first condition further includes one or more of: the resolution ratio of the face area in the image to be sorted is larger than a first threshold value; the face angle in the image to be sorted belongs to a first interval; the position of the face region in the image to be sorted belongs to the second interval.
In one possible implementation, the second determining module 64 is configured to: and randomly selecting one of the candidate images corresponding to the two feature vectors with the highest similarity as a target face image.
In one possible implementation, the second determining module 64 includes: the second determining submodule 641 is configured to determine a face quality score of a candidate image, where the candidate image represents a candidate image corresponding to two feature vectors with the highest similarity; and a third determining sub-module 642, configured to determine the candidate image with the higher face quality score as the target face image.
In one possible implementation, the second determining submodule 641 is configured to: and determining the face quality score of the alternative image according to one or more of the resolution of the face region in the alternative image, the face angle in the alternative image and the position of the face region in the alternative image.
In one possible implementation, the apparatus further includes: and the intercepting module 65 is configured to intercept a face region from the target face image to obtain a face region image of the target person.
In one possible implementation, the apparatus further includes: and the storage module 66 is used for storing the face region image.
In one possible implementation, the apparatus further includes: the establishing module 67 is configured to establish face index data according to the feature vector of the face region image.
In the embodiment of the disclosure, a plurality of candidate images corresponding to a target person are obtained, feature vectors of face regions in the candidate images are respectively extracted, the similarity between the feature vectors of the face regions in different candidate images in the candidate images is determined, and the target face image is determined from the candidate images corresponding to two feature vectors with the highest similarity, so that the face images can be automatically sorted, time and labor are saved, and the image quality of the sorted target face images can be guaranteed.
Fig. 8 is a block diagram illustrating an apparatus 800 for collating face images according to an exemplary embodiment. For example, the apparatus 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.
Referring to fig. 8, the apparatus 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.
The processing component 802 generally controls overall operation of the device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the apparatus 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 808 includes a screen that provides an output interface between the device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 800 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the device 800. For example, the sensor assembly 814 may detect the open/closed status of the device 800, the relative positioning of components, such as a display and keypad of the device 800, the sensor assembly 814 may also detect a change in the position of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, the orientation or acceleration/deceleration of the device 800, and a change in the temperature of the device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communications between the apparatus 800 and other devices in a wired or wireless manner. The device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the device 800 to perform the above-described methods.
Fig. 9 is a block diagram illustrating an apparatus 1900 for collating face images according to an exemplary embodiment. For example, the apparatus 1900 may be provided as a server. Referring to fig. 9, the device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by the processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.
The device 1900 may also include a power component 1926 configured to perform power management of the device 1900, a wired or wireless network interface 1950 configured to connect the device 1900 to a network, and an input/output (I/O) interface 1958. The device 1900 may operate based on an operating system stored in memory 1932, such as Windows Server, MacOS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the apparatus 1900 to perform the above-described methods.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (22)
1. A method for arranging face images is characterized by comprising the following steps:
acquiring a plurality of candidate images corresponding to a target person;
respectively extracting feature vectors of the face regions in the candidate images;
determining similarity between feature vectors of face regions in different candidate images of the plurality of candidate images;
and determining the target face image from the candidate images corresponding to the two feature vectors with the highest similarity.
2. The method of claim 1, wherein obtaining a plurality of candidate images corresponding to the target person comprises:
acquiring a plurality of images to be sorted corresponding to a target person;
and determining a candidate image corresponding to the target person according to the image to be sorted meeting a first condition in the plurality of images to be sorted.
3. The method of claim 2, wherein the first condition comprises: the number of the face regions in the image to be sorted is 1.
4. The method of claim 3, wherein the first condition further comprises one or more of:
the resolution ratio of the face area in the image to be sorted is greater than a first threshold value;
the face angle in the image to be sorted belongs to a first interval;
and the position of the face area in the image to be sorted belongs to a second interval.
5. The method of claim 1, wherein determining the target face image from the candidate images corresponding to the two feature vectors with the highest similarity comprises:
and randomly selecting one of the candidate images corresponding to the two feature vectors with the highest similarity as a target face image.
6. The method of claim 1, wherein determining the target face image from the candidate images corresponding to the two feature vectors with the highest similarity comprises:
determining a face quality score of a candidate image, wherein the candidate image represents a candidate image corresponding to two feature vectors with the highest similarity;
and determining the alternative image with higher face quality score as the target face image.
7. The method of claim 6, wherein determining the face quality score for the candidate image comprises:
and determining the face quality score of the alternative image according to one or more of the resolution of the face region in the alternative image, the face angle in the alternative image and the position of the face region in the alternative image.
8. The method according to claim 1, wherein after determining the target face image from the candidate images corresponding to the two feature vectors with the highest similarity, the method further comprises:
and intercepting a face area from the target face image to obtain a face area image of the target person.
9. The method of claim 8, wherein after obtaining the image of the face region of the target person, the method further comprises:
and saving the face region image.
10. The method according to claim 8 or 9, wherein after obtaining the face region image of the target person, the method further comprises:
and establishing face index data according to the feature vector of the face region image.
11. An arrangement device for human face images is characterized by comprising:
the acquisition module is used for acquiring a plurality of candidate images corresponding to the target person;
the extraction module is used for respectively extracting the characteristic vectors of the face regions in the candidate images;
a first determining module, configured to determine similarity between feature vectors of face regions in different candidate images of the plurality of candidate images;
and the second determining module is used for determining the target face image from the candidate images corresponding to the two feature vectors with the highest similarity.
12. The apparatus of claim 11, wherein the obtaining module comprises:
the acquisition submodule is used for acquiring a plurality of images to be sorted corresponding to the target person;
and the first determining submodule is used for determining a candidate image corresponding to the target person according to the image to be sorted meeting a first condition in the plurality of images to be sorted.
13. The apparatus of claim 12, wherein the first condition comprises: the number of the face regions in the image to be sorted is 1.
14. The apparatus of claim 13, wherein the first condition further comprises one or more of:
the resolution ratio of the face area in the image to be sorted is greater than a first threshold value;
the face angle in the image to be sorted belongs to a first interval;
and the position of the face area in the image to be sorted belongs to a second interval.
15. The apparatus of claim 11, wherein the second determining module is configured to:
and randomly selecting one of the candidate images corresponding to the two feature vectors with the highest similarity as a target face image.
16. The apparatus of claim 11, wherein the second determining module comprises:
the second determining submodule is used for determining the face quality scores of the alternative images, wherein the alternative images represent candidate images corresponding to two feature vectors with the highest similarity;
and the third determining submodule is used for determining the alternative image with higher face quality score as the target face image.
17. The apparatus of claim 16, wherein the second determination submodule is configured to:
and determining the face quality score of the alternative image according to one or more of the resolution of the face region in the alternative image, the face angle in the alternative image and the position of the face region in the alternative image.
18. The apparatus of claim 11, further comprising:
and the intercepting module is used for intercepting a face area from the target face image to obtain a face area image of the target person.
19. The apparatus of claim 18, further comprising:
and the storage module is used for storing the face region image.
20. The apparatus of claim 18 or 19, further comprising:
and the establishing module is used for establishing face index data according to the characteristic vector of the face region image.
21. An arrangement device for human face images is characterized by comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the method of any one of claims 1 to 10.
22. A non-transitory computer readable storage medium having stored thereon computer program instructions, wherein the computer program instructions, when executed by a processor, implement the method of any one of claims 1 to 10.
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