AU2021104568A4 - University surveillance and attendance system using face recognition based on machine learning and internet of things - Google Patents
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Abstract
University surveillance and attendance system using face recognition based on machine
learning and internet of things focuses to recognize a face over different angles accurately
through the method HOG. Histogram of Oriented Gradients (HOG) as the detection model
since it is fast but not very accurate since it is statistical feature extraction. This has many
methods the most popular are CNN and HOG. The HOG is an image patch that simplifies
the image by extracting useful information and throwing away extraneous information used
in computer vision and image processing for object detection. The design of the Face
recognition system can also be done by model training. We can train the images by creating
datasets, preprocessing, feature extraction, model training, validation, and prediction. We
have used the Machine Learning approach of training Support Vector Machine with wavelet
transformation is a feeder to the model as wavelet transformation localizes images and can
detect abrupt changes of any shape. The model training can be validated over new images
and save in a pickle file for prediction from the saved model. This technique helps to
increase data if required depending on performance or hyperparameter tuning to increase the
efficiency of model accuracy. We used the Support Vector Machine (SVM) algorithm where
we have sent cropped raw images long with wavelet transformation to train the classifier. So
that the model will learn the global features in the image and can accurately predict the
outcome with appropriate accuracy.
1/6
All the irnages trained in the dataset are detected
ppj the nage which is not trained in the data set
Prop o zed C onvolution Neural Network Mo deI
Original Image, Pre-processed Inage and Confusion Matrix
FIG. 1
TRAININC PHASE TESING PHASE
-tlrolgr ralpbeirp
a irm rages
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arieiractcati.C
iplioilynelmali- yo
Tralmnig rene9 b
creein9 preprocesseo
k ing iho'Jgha
FI2ba
FIG. 2
Description
1/6
All the irnages trained in the dataset are detected ppj the nage which is not trained in the data set
Prop o zed C onvolution Neural Network Mo deI
Original Image, Pre-processed Inage and Confusion Matrix
FIG. 1
-tlrolgr ralpbeirp
a irm rages
Up~ ...r- -S tle arieiractcati.C iplioilynelmali- yo
Tralmnig rene9 b creein9 preprocesseo
k ing iho'Jgha FI2ba
FIG. 2
[0001] The present invention relates to facial recognition systems and in particular to facial recognition systems for implementing a surveillance and attendance system.
[0002] The invention has been developed primarily for use as a university surveillance and attendance system using face recognition and will be described hereinafter with reference to this application. However, it will be appreciated that the invention is not limited to this particular field of use.
[0003] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0004] The human face plays an important role in our social interaction, conveying people's identity. Using the human face as a key to security, biometric face recognition technology has received significant attention in the past several years due to its potential for a wide variety ofapplications. As compared with other biometrics systems using fingerprint and iris, face recognition has distinct advantages because of its non contact process. Face images can be captured from a distance without touching the person being identified, and the identification does not require interacting with the person.
[0005] Besides, face recognition serves the crime deterrent purpose because face images that have been recorded and archived can later help identify a person. Many Existing Systems can identify the face from a different angle but cannot overlay the person's identity when recognized. Therefore, the systems which are beingdeveloped in the present environment are focusing on how to develop such intelligent systems so that the security, biometrics can be far improved.
[0006] Many different types of university surveillance and attendance systems are known in the prior art. For example, the following patents are provided for their supportive teachings and are all incorporated by reference.
[0007] US20100111377AI Embodiments provide a surveillance system having at least one camera adapted to produce an IP signal, at least one camera having an image collection device configured for collecting image data, the at least one camera having at least one facial processor configured to execute with digital format image data at least one facial recognition algorithm, execution of the at least one facial recognition algorithm with the digital format image data detecting faces when present in the digital format image data, execution of the at least one facial recognition algorithm providing for each detected face at least one set of unique facial image data.
[0008] Face Recognition-based smart attendance system using IOT Attendance is a compulsory requirement of every organization. Maintaining the attendance register daily is a difficult and time-consuming task. There are many automated methods for the same available like biometrics, RFID, eye detection, voice recognition, and many more. This paper provides an efficient and smart method for marking attendance. As it is known that the primary identification for any human is its face, face recognition provides an accurate system that overcomes the ambiguities like fake attendance, high cost, and time consumption. This system uses a face recognizer library for facial recognition and storing attendance. The absentee's supervisor or parents are informed through email regarding the absence of their employees or wards respectively. The objective of this project is to innovate existing projects with some added features like large data storage and fast computing through less hardware cost.
[0009] Facial recognition technology in schools: critical questions and concerns Facial recognition technology is now being introduced across various aspects of public life. This includes the burgeoning integration of facial recognition and facial detection into compulsory schooling to address issues such as campus security, automated registration, and student emotion detection. So far, these technologies have largely been seen as routine additions to school systems with already extensive cultures of monitoring and surveillance. While critical commentators are beginning to question the pedagogical limitations of facially driven learning, this article contends that school-based facial recognition presents several other social challenges and concerns that merit specific attention. This includes the likelihood of facial recognition technology altering the nature of schools and schooling along divisive, authoritarian, and oppressive lines. Against this background, the article considers whether or not a valid case can ever be made for allowing this form of technology in schools.
[0010] Facial Recognition is biometric authentication software that can verify a person uniquely by analyzing patterns based on the person's facial contours. The main aim is to recognize a face over different angles accurately through the method HOG. Histogram of Oriented Gradients (HOG) as the detection model since it is fast but not very accurate since it is statistical featureextraction. This has many methods the most popular are CNN and HOG.
[0011] The above information is presented as background information only to assist with an understanding of the present disclosure. No determination has been made, no assertion is made, and as to whether any of the above might be applicable as prior art about the present invention.
[0012] Because of the foregoing disadvantages inherent in the known types of university surveillance and attendance system now present in the prior art, the present invention provides an improved system. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved face recognition based on machine learning for surveillance of university and attendance that has all the advantages of the prior art and none of the disadvantages.
[0013] It is an object of the present invention to overcome or ameliorate at least one of the disadvantages of the prior art, or to provide a useful alternative.
[0014] It is an object of the invention in its preferred form to provide a university surveillance and attendance system using face recognition.
[0015] According to the invention there is provided a university surveillance and attendance system using face recognition based on machine learning and internet of things, the system including: a plurality of trained image data sets; a convolution neural network model; and a confusion matrix.
[0016] Preferably, the data sets are images of students and faculty members of a particular university, which are used to train the machine learning model.
[0017] Preferably, the convolution neural network model compares and contrasts a captured image with the trained image data set of already captured and preprocessed images.
[0018] Preferably, the confusion matrix compares and contrasts original and preprocessed images to give the resultant matrix that marks the attendance of a student or faculty member.
[0019] Preferably, the system provides an authenticated attendance and surveillance system that can be automated.
[0020] It will be appreciated that he present technology relates to the field of designing and implementing the University Surveillance and Attendance System (USAS) using Face Recognition based on Machine Learning and the Internet of Things. It is mainly focused to develop with Open-CV which supports Computer Vision which is an interdisciplinary scientific field that deals with how computers can be made to gain high-level understanding from digital images or videos using Internet of Things devices.
[0021] Because of the foregoing disadvantages inherent in the known types of university surveillance systems now present in the prior art, the present technology provides an improved face recognition-based Machine learning approach. As such, the general purpose of the present technology, which will be described subsequently in greater detail, is to provide a new and improved university surveillance system that has all the advantages of the prior art and none of the disadvantages.
[0022] A main aspect of the proposed technology is to recognize a face over different angles accurately through the method HOG. Histogram of Oriented Gradients (HOG) as the detection model since it is fast but not very accurate since it is statistical featureextraction. This has many methods the most popular are CNN and HOG.
[0023] Yet another aspect of the proposed technology is that the HOG is an image patch that simplifies the image by extracting useful information and throwing away extraneous information used in computer vision and image processing for object detection. The hog is a faster process but gives a statistical review to the face where the encoding is 60% accurate. CNN image classifications give an accurate encoding percentage of around 87.92% as per the facial embedding.
[0024] Yet another important aspect of the proposed technology is that the face recognition concept has many modules to be proved over security, attendance, and many others. The proposed technology mainly focuses to develop with Open-CV. It can also detect faces from ID cards to give access to a particular room. This can be implemented on a dedicated raspberry pi to get better accuracy. Whereas the SupportVector Machine (SVM) plays an important role in face recognition and it has an accuracy of 99.63% when compared with all previous techniques and methods.
[0025] In this respect, before explaining at least one embodiment of the technology in detail, it is to be understood that the technology is not limited in its application to the details of construction and the arrangements of the components outlined in the following description or illustrated in the various ways. Also, it is to be understood that the phraseology and terminology employed herein are for description and should not be regarded as limiting.
[0026] These together with other objects of the invention, along with the various features of novelty that characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages, and the specific objects attained by its uses, reference should be had to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
[0027] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description refers to the annexed drawings wherein:
FIG. 1 illustrates the Architecture of University Surveillance and Attendance System (USAS) of university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein; FIG. 2 illustrates the Flowchart Methodology for Face Recognition of university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein. FIG. 3 illustrates the Image Dataset-lof university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein; FIG. 4 illustrates the image dataset-2 of university surveillance and attendance system using face recognition based on machine learning and the internet of things,
1; according to the embodiment herein; FIG. 5 illustrates the image dataset-3 of university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein; FIG. 6 illustrates the All the faces in the image are trained to show 128-d embeddings trained in CNN and HOG method of university surveillance and attendance system using face recognition based on machine learning and internet of things, according to the embodiment herein; FIG. 7 illustrates the All the images trained in the dataset are detected except the image which is not trained in the dataset of university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein; FIG. 8 illustrates the Test for video recognition where the faces are recognized through Webcam and a frame is recognized for each second in different angles by comparing with the encoded faces of university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein; FIG. 9 illustrates the Faces are recognized through the CNN method here the Webcam is placed at a distance so that it could recognize accurately. The main aim of the above phase is to test if the faces in CCTV footage can be recognized when executed by university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein; FIG. 10 illustrates the face recognition through video of university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein; FIG. 11 illustrates the (a) Original Image (Mr. M. Sameeri), (b) Pre-processed Image of university surveillance and attendance system using face recognition based on machine learning and internet of things, according to the embodiment herein; and FIG. 12 illustrates the Confusion Matrix for face Recognition of university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein.
[0028] In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that the embodiments may be combined, or that other embodiment may be utilized, and that structural and logical changes may be made without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims and their equivalents.
[0029] While the present invention is described herein by way of example using several embodiments and illustrative drawings, those skilled in the art will recognize that the invention is neither intended to be limited to the embodiments of drawing or drawings described nor intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention covers all modification/s, equivalents, and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. The headings are used for organizational purposes only and are not meant to limit the scope of the description or the claims. As used throughout this description, the word "may" be used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Further, the words "a" or "a" mean "at least one" and the word "plurality" means one or more unless otherwise mentioned. Furthermore, the terminology and phraseology used herein are solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and any additional subject matter not recited, and is not intended to exclude any other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents acts, materials, devices, articles, and the like are included in the specification solely to provide a context for the present invention.
[0030] In this disclosure, whenever an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same element or group of elements with transitional phrases "consisting essentially of, "consisting", "selected from the group consisting of', "including", or "is" preceding the recitation of the element or group of elements and vice versa.
[0031] University surveillance and attendance system using face recognition based on machine learning and internet of things focuses to recognize a face over different angles accurately through the method HOG. Histogram of Oriented Gradients (HOG) as the detection model since it is fast but not very accurate since it is statistical feature extraction. This has many methods the most popular are CNN and HOG. The HOG is an image patch that simplifies the image by extracting useful information and throwing away extraneous information used in computer vision and image processing for object detection. The design of the Face recognition system can also be done by model training. We can train the images by creating datasets, preprocessing, feature extraction, model training, validation, and prediction. We have used the Machine Learning approach of training Support Vector Machine with wavelet transformation is a feeder to the model as wavelet transformation localizes images and can detect abrupt changes of any shape. The model training can be validated over new images and save in a pickle file for prediction from the saved model. This technique helps to increase data if required depending on performance or hyperparameter tuning to increase the efficiency of model accuracy. We used the Support Vector Machine (SVM) algorithm where we have sent cropped raw images long with wavelet transformation to train the classifier. So that the model will learn the global features in the image and can accurately predict the outcome with appropriate accuracy of 99.63%.
[0032] In many applications like surveillance and monitoring, the traditional biometric techniques will fail as for obvious reasons one cannot ask everyone to come and put his/her thumb on a slide or something similar. So, there is a need for a system that is similar to the human eye in some sense to identify a person.
[0033] To cater to this need and using the observations of human psychophysics, face recognition has emerged. Different approaches have been tried by several groups, working worldwide, to solve this problem. Many commercial products have also found their way into the market using one or the other technique. But so far, no system/technique exists which has shown satisfactory results in all circumstances.
[0034] A comparison of these techniques needs to be done. This work only shows a comparison of already made research studies; therefore, the pictures used and the data are extracted from the sources. Appearance-based face recognition techniques have received significant attention from a wide range of research areas such as biometrics, pattern recognition, and computer vision. Specifically, there are two categorizations implied viz. Holistic and Hybrid approaches. The holistic approach uses the whole face region as the raw input to a recognition system. It also attempts to capture the most appropriate representation of face images as a whole and exploit the statistical regularities of pixel intensity variations. One of the most widely used representations of the face region is Eigen pictures, which are based on the principal component analysis. The other category contains hybrid approaches, just as the human perception system uses both local features and the whole face region to recognize. a face, a machine recognition system should use both.
[0035] The proposed technology is mainly focused to develop with Open-CV. It can also detect faces from ID cards to give access to a particular room. This can be implemented on a dedicated raspberry pi to get better accuracy. Whereas the SupportVector Machine (SVM) plays an important role in face recognition and has an accuracy of 99.63% when compared with all previous techniques and methods.
[00361 Feature-based face recognition uses a priori information or local features of faces to select several features to exclusively identify individuals. Local features include the eyes, nose, mouth, chin, and head outline, which are selected from face images. Topological graphs are used to represent relations between features, and a simple deterministic graph-matching scheme that exploits the basic structure is used to distinguish familiar faces from a database.
Module-i:
[00371 This implementation has three phases where the first part is to maintain a dataset through which an image can be retrieved with a recognized face. The image should be encoded by encoding () function in python by NumPy array and saving it in a pickle file which converts encodings to make sure whether the person is the required target to be recognized. Since each image cannot be encoded every time reparsing is quite a good technique where a query can execute and give the required output which the user requires. Many images can be encoded and stored in the array in a. pickle extension.
Module-2:
q
[00381 The next phase is to recognize an image in the dataset. To do the task Computer Vision is the main concept to move further in the implementation. Secondly, an image is converted into a frame in which the pickle module provides the interface to recognize the image. The main idea is to read the image convert the image into matrix distribution in a 3D array and that is compared with the dataset and would give the name of the image surrounded by the box. Functions like reading () are used to read the image, show () to show the image from CV2 and finally, the encodings are matched with the names with the required index. A dictionary is set to count the total number of times each face was matched with the given encodings and thus would provide the required output image.
Module-3:
[00391 This is the challenging phase where the image should be recognized over a video stream. The same encodings should be matched with the following video stream where the face in the image is recognized. Since the video streaming requires a high-quality Webcam/D3D CCCamera, therefore code is changed to produce frames, and recognition of the image becomes easy. Through this module called utils, the video stream breaks the video into the frame for every second and recognizes the name of the person from different angles.
[00401 Reference will now be made in detail to the exemplary embodiment of the present disclosure. Before describing the detailed embodiments that are under the present disclosure, it should be observed that the embodiment resides primarily in combinations arrangement of the system according to an embodiment herein and as exemplified in FIG. 1
[0041] FIG. 1 illustrates the Architecture of University Surveillance and Attendance System (USAS) of university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein. The architecture comprises trained data sets that are fed to the proposed convolution neural network for recognition.
[0042] FIG. 2 illustrates the Flowchart Methodology for Face Recognition of university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein. The flowchart comprises training and testing phases.
[0043] FIG. 3 illustrates the Image Dataset-lof university surveillance and attendance system
1n using face recognition based on machine learning and the internet of things, according to the embodiment herein. The images of the students and faculty members are captured from various angles and used to train the data set.
[0044] FIG. 4 illustrates the image dataset-2 of university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein. The second set of images as data sets are used to train the machine learning system.
[0045] FIG. 5 illustrates the image dataset-3 of university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein. The second set of images as data sets are used to train the machine learning system.
[0046] FIG. 6 illustrates the All the faces in the image are trained to show 128-d embeddings trained in CNNand HOG method of university surveillance and attendance system using face recognition based on machine learning and internet of things, according to the embodiment herein.
[0047] FIG. 7 illustrates the All the images trained in the dataset are detected except the image which is not trained in the dataset of university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein.
[0048] FIG. 8 illustrates the Test for video recognition where the faces are recognized through Webcam and aframe is recognized for each second in different angles by comparing with the encoded faces of university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein.
[0049] FIG. 9 illustrates the Faces are recognized through the CNN method here the Webcam is placed at a distance so that it could recognize accurately. The main aim of the above phase is to test if the faces in CCTV footage can be recognized when executed by university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein.
[0050] FIG. 10 illustrates the face recognition through video of university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein.
[0051] FIG. 11 illustrates the (a) Original Image (Mr. M. Sameeri), (b) Pre-processed Image of university surveillance and attendance system using face recognition based on machine learning and internet of things, according to the embodiment herein.
[0052] FIG. 12 illustrates the Confusion Matrix for face Recognition of university surveillance and attendance system using face recognition based on machine learning and the internet of things, according to the embodiment herein.
[00531 In the following description, for an explanation, numerous specific details are outlined to provide a thorough understanding of the arrangement of the system according to an embodiment herein. It will be apparent, however, to one skilled in the art that the present embodiment can be practiced without these specific details. In other instances, structures are shown in block diagram form only to avoid obscuring the present invention.
Claims (5)
1. A university surveillance and attendance system using face recognition based on machine learning and internet of things, the system including: a plurality of trained image data sets; a convolution neural network model; and a confusion matrix.
2. The system according to claims 1, wherein the data sets are images of students and faculty members of a particular university, which are used to train the machine learning model.
3. The system according to any one of the preceding clams, wherein the convolution neural network model compares and contrasts a captured image with the trained image data set of already captured and preprocessed images.
4. The system according to any one of the preceding clams, wherein the confusion matrix compares and contrasts original and preprocessed images to give the resultant matrix that marks the attendance of a student or faculty member.
5. The system according to any one of the preceding clams, wherein the system provides an authenticated attendance and surveillance system that can be automated.
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