Facial Recognition In Modern Security Systems

Approaches to Enable Facial Recognition

The modern security systems incorporate the use of facial recognition as a way of enabling authorization. This can be achieved in personal identification for instance, at the immigration office or border points, for human computer interactions such as phone unlock using facial recognition, and major security systems for instance, in offices or large residential complex or penthouses (Navarrete & Ruiz-del, 2002). Every human being has very complex, multidimensional, and meaningful facial unique attributes that differentiate them from others. This makes the facial recognition process more difficult to implement in security systems. Some of the local features that the recognition process focusses on are the eyes, nose, and mouth before extracting the feature of the whole face (Charalampos & Ilias, 2010).

There are a number of approaches defined to enable the facial recognition by systems. For instance, using artificial intelligence, one can create neural networks and self-organizing maps, SOMs, where the user trains some images and uses test images to test the recognition. Other alternatives to this approach are content-based image retrieval, principal component analysis and the relevance feedback (Turk & Pentland). There are three stages performed in the process of facial recognition such as the face location detection, feature extraction, and facial image classification. The face recognition is done using eigenface algorithm (Emad, Tamer, & AbdelMonem). The face images are projected into a feature space that best encodes the variation among the known face images. The face space is well expounded by the eigenfaces, which are the eigenvectors of the set of faces. The eigenface algorithm computes the average face, v. The algorithm collects the difference between training images and the average face (Ruiz-del-Solar & and Navarrete, 2002). The differences are saved in a matrix where M is the number of pixels and N is the number of stored or trained images. The algorithm for eigenfaces is denoted by the equation, 

The eigenvectors of the covariance matrix C are used to give the final eigenfaces. This is done using powerful tools with a stable runtime such as MATLAB R2017a. Therefore, 

There are N-1 meaningful eigenvectors, when the number of data points is smaller than the dimensions. To get a faster response on the value of the eigenvectors of C, 

The training face images and the new face images are represented as a linear combination of the eigenfaces. For instance, a face image, u, can be represented as, 

The eigenvectors are usually orthogonal to the eigenvalues such that, 

Stages of Facial Recognition

The PCA seeks directions that are efficient for the representation of the data and seeks to maximize the total scatter. The PCA reduces the dimension of the data and speeds up the computational time. The time taken to perform facial recognition is important especially in implementation in the actual environment such that the systems should use the least time to detect a face (Tolba, El-Baz, & El-Harby, 2006). It should be close to real-time. 

The task aims at using the training and testing data to identify and extract face images from the image saved. This is done when the algorithm pulls similar images from the database with a set of 30 images from the training data and 20 images from the test data.

• The training set is obtained as the 30 images and the test set has 20 images all defined in set folders. The algorithm is loaded and the eigenfaces are calculated using the PCA projections. These projections define the eigenspace.
• The new face is checked by using the test data and the weight of the connections or links is computed. The system, at this point, determines if the image used is a face. When the algorithm identifies the new image as a face, the weight pattern is grouped under the known or unknown(Hongliang, Qingshan, Xiaoou, & Hanqing, 2005).
• In practice, the grouping would be used to grant or deny authorization or access to a building. The image should match an existing image in the database for it to be successfully recognized(Belhumeur, Hespanha, & Kriegman).
• The learning process during training seeks to pick the unknown faces and incorporate them in the database. For instance, during the registration of a new staff member in a company, the system learns and it adds the unknown face to its database alongside some description to grant or deny access to the given face(Chellappa, Wilson, & Sirohey).

The data is obtained from the lab experiments with student images. The training set contains 10 students with 3 moods each. The resulting matrix is 10×3 images while the test set contains 10×2 images where the 10 test subjects have 2 moods each. The designer creates a face database, the system generates the eigenfaces database and creates the top eigenfaces (Teofilo, Rogerio, & Roberto, 2000). The test image is used to show if the system can recognize the face from the already setup database.

• The first attempt seeks to classify the average coefficient for each person such that the new face is compared to the closest average. The recognition accuracy increases with more learning procedures.
• The eigenfaces are useful during training to set the average value of recognition but do not help much afterwards.

%% Part 1: Resizing and Reading Images 

% Subject1-Dhara

dhara=imread(‘D:ONLINE WORK725331assignmentTrainingDhara_Happy.jpg’,’jpg’);

dhara=rgb2gray(dhara);

dhara=imresize(dhara,[N N1] );

figure(1),imshow(dhara,’Initialmagnification’,’fit’);

title(‘dhara’) 

% Subject2-Diksha

diksha=imread(‘D:ONLINE WORK725331assignmentTrainingDiksha_Happy.jpg’,’jpg’);

diksha=rgb2gray(diksha);

diksha=imresize(diksha,[N N1] );

figure(2),imshow(diksha,’Initialmagnification’,’fit’);

title(‘dhara’) 

% Subject3-Eric

Eric=imread(‘D:ONLINE WORK725331assignmentTrainingEric_Happy.jpg’,’jpg’);

Eric=rgb2gray(Eric);

Eric=imresize(Eric,[N N1] );

figure(3),imshow(Eric,’Initialmagnification’,’fit’);

title(‘Eric’)

% Subject4-Gauta

Gauta=imread(‘D:ONLINE WORK725331assignmentTrainingGautam_Happy.jpg’,’jpg’);

Gauta=rgb2gray(Gauta);

Gauta=imresize(Gauta,[N N1] );

figure(4),imshow(Gauta,’Initialmagnification’,’fit’);

title(‘Gauta’) 

% Subject5-Ghaida

Ghaida=imread(‘D:ONLINE WORK725331assignmentTrainingGhaida_Happy.jpg’,’jpg’);

Ghaida=rgb2gray(Ghaida);

Ghaida=imresize(Ghaida,[N N1] );

figure(5),imshow(Ghaida,’Initialmagnification’,’fit’);

title(‘Ghaida’)

% Subject6-Haoyang

Haoyang=imread(‘D:ONLINE WORK725331assignmentTrainingHaoyang_Happy.jpg’,’jpg’);

Haoyang=rgb2gray(Haoyang);

Haoyang=imresize(Haoyang,[N N1] );

figure(6),imshow(Haoyang,’Initialmagnification’,’fit’);

title(‘Haoyang’)

% Subject7-Huipei

Huipei=imread(‘D:ONLINE WORK725331assignmentTrainingHuipei_Happy.jpg’,’jpg’);

Huipei=rgb2gray(Huipei);

Huipei=imresize(Huipei,[N N1] );

figure(7),imshow(Huipei,’Initialmagnification’,’fit’);

title(‘Huipei’)

% Subject8-Hung

Hung=imread(‘D:ONLINE WORK725331assignmentTrainingHung_Happy.jpg’,’jpg’);

Hung=rgb2gray(Hung);

Hung=imresize(Hung,[N N1] );

figure(8),imshow(Hung,’Initialmagnification’,’fit’);

title(‘Hung’) 

% Subject9-James

James=imread(‘D:ONLINE WORK725331assignmentTrainingJames_D_Happy.jpg’,’jpg’);

James=rgb2gray(James);

James=imresize(James,[N N1] );

figure(9),imshow(James,’Initialmagnification’,’fit’);

title(‘James’) 

% Subject10-Neel

Neel=imread(‘D:ONLINE WORK725331assignmentTrainingNeel_Happy.jpeg’,’jpg’);

Neel=rgb2gray(Neel);

Neel=imresize(Neel,[N N1] );

figure(10),imshow(Neel,’Initialmagnification’,’fit’);

title(‘Neel’)

Table 1 Results for the test dataset (K1)

	1-NN	3-NN	5-NN
40×30 size	39.0415	36.0745	45.6723
80×60 size	78.0830	72.1490	89.3447
Average	58.5622	54.1118	67.5085

Table 2 Results for the test dataset (K2)

	1-NN	3-NN	5-NN
40×30 size	43.3975	40.4305	50.0283
80×60 size	82.439	76.505	93.7007
Average	62.9182	58.4678	71.8645

Part 3: Training and Test dataset results from classifier output

The best rates that can be used in a high performing system are such as, 

The neural networks model is good with training the data but it takes some time to perform the training especially if the data set is too large. When the classification is performed using the nearest neighbor technique, it takes more classification time. When the test case is chosen as Dhara is surprised, the algorithm links the image to the original image of Dhara as taken during training.

References 

Belhumeur, P., Hespanha, & Kriegman, D. (n.d.). Eigenfaces vs Fisherfaces. Recognition using Class Specific Linear Proection.

Charalampos, D., & Ilias, M. (2010). A fast-mobile face Recognition System for Android OS Based on Eigenfaces Decomposition. IFIP Advances in Information and Communication Technology, 295-302.

Chellappa, R., Wilson, C. L., & Sirohey, C. (n.d.). Human and Machine recognition of faces: A Survey. Proceedings of IEEE, 705-740.

Emad, B., Tamer, M., & AbdelMonem, W. A. (n.d.). A new image comparing technique for content-based image retrieval.

Hongliang, i., Qingshan, L., Xiaoou, T., & Hanqing, L. (2005). Learning Local Descriptors for Face Detection: Multimedia and Expo. IEEE International Conference on 06-06 2005; ICME 2005, 928-931.

Navarrete, P., & Ruiz-del, S. (2002). Interactive Face Retrieval using Self-Organizing Maps. International Joint Conference on Neural Networks-IJCNN 2002, 12-17.

Ruiz-del-Solar, J., & and Navarrete, P. (2002). Towards a Generalized Eigenspace-based Face Recognition Framework. 4th Int. Workshop on Statistical Techniques in Pattern Recognition, 6-9.

Teofilo, E. d., Rogerio, S. F., & Roberto, C. M. (2000). First steps toward performance assessment of representation for Face Recognition Lecture notes in Artificial Intelligence. Eigenfaces versus Eigeneyes, 197-206.

Tolba, A. S., El-Baz, A. H., & El-Harby, A. A. (2006). Face Recognition: A literature Review. Internation Journal of Signal Processing, 2-5.

Turk, M., & Pentland, A. (n.d.). Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 71-86.