CHAPTER 6 PERFORMANCE ANALYSIS OF SOFT COMPUTING BASED FACE RECOGNITION

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1 137 CHAPTER 6 PERFORMANCE ANALYSIS OF SOFT COMPUTING BASED FACE RECOGNITION In the previous chapters, a brief description about the theory and experimental results of PCA, FLDA, Gabor, Neural, Db wavelets and Fuzzy C-means clustering based algorithms for human Face recognition are presented. In this chapter, some description about performance metrics (Shepherd et al 1991) of Face Recognition like Recognition rate, Execution time, False Acceptance Ratio (FAR) and False Rejection Ratio (FRR) for all the above algorithms are discussed. 6.1 PERFORMANCE ANALYSIS In this section six algorithms for face recognition under different aspects of light, pose and facial expressions are compared based on some performance metrics like recognition rate, execution time and FAR and FRR (Sherrah 2004). In Chapters 3, 4 and 5, various algorithms of Face recognition using Eigenfaces method, and the Fisher face method, Gabor wavelets, Back propagation Neural Network, Cascaded Neural network, Fuzzy Integral and Fuzzy C- means Clustering are discussed. The experimentation is applied on three important data bases like ORL, YALE and FERET. Finally, it is concluded that the Fuzzy C means clustering with BPNN method has lower error rate than any of the other methods. Apart from this, the successful implementation of these algorithms involves huge training set with multiple images in different pose and

2 138 expression for each person. Also, the performance of these algorithms varies under different training set and different test set. The performance metrics like Recognition rate, Accuracy, FAR, FRR and Execution time are calculated as per the formulas given in equations (3.15) to (3.17) Recognition Rate Recognition Rates (RR) are compared for all the algorithms in Chapters 2, 3, 4 and it is concluded that the algorithm that combines FLDA, FCM and BPNN achieves the best. False Acceptance Rate (FAR) is that each of the test images of a single subject is compared with all images of all other subjects but not its own images. False rejection rate (FRR) is that each of the test images of a single subject is compared with each of the training image of that subject. No image is compared with itself (i.e. training and testing images should be different) and each pair is compared only once. Results are compared using the RR, FAR, FRR and Accuracy. The two standard biometric measures, FAR and FRR indicate the identifying power. The lower the equal value, the better identifying power the system possesses Execution Time For all the above algorithms, execution time (i.e.) total time taken to recognize the given image in seconds is compared and it ranges from seconds for 50 to 400 images. Among the algorithms, the combination of FLDA, FCM Gabor and BPNN is found to be having less execution time. In MATLAB total execution time is measured by the program using Tic and Tac commands Accuracy For all the above algorithms, overall accuracy is calculated and it is found to be around 90 % for sixty images. The best accuracy is obtained from

3 139 the FLDA, FCM and BPNN. The comparison of the performance parameters with all above said algorithm are experimentally verified with all databases. Tabulation and Graphical analysis of ORL data base for recognition rate, execution time and accuracy are presented in Tables 6.1, 6.2, 6.3 and Figures 6.1, 6.2, 6.3 respectively. Table 6.1 Comparison of recognition rate for all algorithms Recognition rate (%) No. of images PCA FLDA BPNN CNN DB+FLDA FCM+BPNN Figure 6.1 Graphical analysis of all algorithms for recognition rate

4 140 Table 6.2 Comparison of execution time for all algorithm No. of Images Execution time (sec) PCA FLDA BPNN CNN DB +FLDA FCM+BPNN Figure 6.2 Graphical analysis of execution time for all algorithms

5 141 False acceptance ratio (FAR) is an instance where the system traces incorrect identity and false rejections ratio (FRR) is an instance where the system rejects a valid identity. Accuracy is calculated using FAR & FRR as follows. Accuracy = [1-(FAR+FRR) /2] * 100 % ---- (6.1) Table 6.3 Overall accuracy of all algorithms Sl. No No of Images Accuracy (%) Authorized trials Unauthorized trials ORL Yale FERET Real time images Figure 6.3 Graphical analysis of accuracy for all algorithms

6 OVERALL COMPARISON Various types of algorithms applied in face recognition technology have their own significance, problems and complications. In this section main features, advantages and disadvantages of PCA, FLDA, GW, BPNN, FI and FCM based algorithms are discussed. Among all these FLDA, FCM, BPNN are found to have enhanced recognition. Principal Component Analysis (PCA) is a standard tool in modern data analysis and it is a simple, non-parametric method for extracting relevant information from the confusing data sets. Main advantages of PCA are its ability to recognize fast and its easy implementation. Eigenfaces algorithm has some shortcomings due to the use of image pixel gray values. In PCA, the size and location of each face image must remain the same. Different illumination, head pose and Facial Expressions lead to reduced recognition rate. The PCA approach typically requires the full frontal face to be presented each time; otherwise the image results in poor performance. Although the face recognition results were acceptable, the system using only eigenfaces might not be applicable as a real system. It needs to be more robust and to have other Discriminant features. PCA is Translation variant, Scale variant, Background variant, lighting variant. PCA derives the most expressive and inspired features, and provides a good discrimination. Finally, it is focused that speed and efficiency is high in PCA and it is suitable for Linear, but not suitable for Dissimilar, different lighting and poses. Fisher face method provides better ability to recognize a face and provides better discrimination between faces. FLDA works well for different illumination and different facial expressions. This statistically motivated method maximizes the ratio of the determinant of between-class scatter matrix

7 143 and within-class scatter matrix and in this sense attempts to involve information about classes of the patterns under consideration providing lower error rate. The solution for recognition of facial image in FLDA, involves segmentation of faces from the cluttered scenes or background, extraction of feature vector from the face region, identification and matching. The images used in this work can have variations in head orientations, scaling and lighting. This approach requires a high degree of correlation between the pixel intensities of the sample and the test images. FLDA approach has limitations over the variations in light, size and in the head orientation. Nevertheless, this method showed very good classifications of faces. A good recognition system should have the ability to adapt over time. A difficulty in using the FLDA method for face recognition is the high-dimensional nature of the image vector. In this FLDA technique, variations in illumination and expressions are reduced substantially. Speed and efficiency is high but it is not efficient for small databases and linear problems. Gabor wavelet provides the optimized resolution in both time and frequency domains for time frequency analysis. As it saves neighborhood relationship between pixels, it performs better than other traditional approaches in terms of efficiency and accuracy. It is invariant to homogenous illumination changes, rotational and scale. Gabor filters have fast recognition and low computational cost, Due to its biological similarity to human vision system, Gabor wavelets have been widely used in object recognition applications like fingerprint recognition, character recognition, etc. Gabor wavelet representations are among the most successful mathematical tools and are

8 144 mainly applied in signal analysis, image processing and many other information-related areas. Despite the success of Gabor wavelet based face recognition systems, both the feature extraction process and the huge dimension of Gabor features extracted demand large computation and memory costs, which makes them impractical for real applications. Another limitation in the case of Gabor wavelets is that the time for Gabor feature extraction is very long and its dimension is prohibitively large. Neural classification is suitable for both linear and nonlinear problems. It has the advantages of high accuracy, high recognition rate and easy to implement. BPNN is a gradient descent based algorithm and it is difficult to train and training time is more, but the execution time is less.also some inherent difficulties like slow convergence and difficulty in escaping from a local minimum are encountered. In BPNN, large number of neurons in the hidden layer can give high generalization error due to over fitting and high variance. Cascade-Correlation is much useful for incremental learning, in which new information is added to an already-trained net. There is no need to guess the size, depth and connectivity pattern of the network in advance. A reasonably small net is built automatically. It may be possible to build networks with a mixture of nonlinear unit-types. Cascade-Correlation learns fast. In backpropogation, the hidden units engage in a complex way before they settle into distinct useful roles; in Cascade-Correlation, each unit sees a fixed problem and can move decisively to solve that problem. Cascade-Correlation can build deep nets without the dramatic slowdown seen in back-propagation networks with more than one or two hidden layers

9 145 In CNN, there is no need to propagate error signals backwards through the network connections as in BPNN. As cascade-correlation network builds hidden units into the neural network as it goes, the neural network designer is relieved of the task of having to guess at the best configuration of hidden units for a particular problem. Cascade-correlation can converge quickly. As the Training time is very fast, it makes cascade correlation networks suitable for large training sets. As with all types of models, there are some disadvantages to cascade correlation networks. They have an extreme potential for over fitting the training data; this results in excellent accuracy on the training data but poor accuracy on new, unseen data. Cascade correlation networks usually are less accurate than probabilistic and general regression neural networks on small to medium size problems (i.e., fewer than a couple of thousand training rows). Cascade correlation is less likely to get trapped in local minima than multilayer perceptron networks. Thus this DB wavelets, FLDA and FI method, in comparison with other methods, has the advantages of low computational cost and requires less time for classification for small number of database images. Further, the variation in illumination, pose or facial expression does not affect the recognition process. The fuzzy integral data fusion technique also has its limitation. The number of fuzzy measures increases exponentially with the number of parameters. One of the distinguishing features of fuzzy measure and fuzzy integral technique is that it is able to represent certain interactions between criteria. Finally, it has been experimentally demonstrated that the aggregation of classifiers resulting from fisher face operating on four sub image sets generated by wavelet decomposition leads to better classification

10 146 results. In this method, images under different illumination and expressions can be recognized. FCM has the advantages of low computational cost and requires less time for classification. Further, the variation in illumination, pose or facial expressions does not affect the recognition process. The fuzzy integral data fusion technique also has its limitation. The number of fuzzy measures increases exponentially with the number of parameters. The Fuzzy C-means (FCM) algorithm has successfully been applied to a wide variety of clustering problems. However, these algorithms work better for unlabeled data, for this project an algorithm which can utilize the labels of the data is needed for a better performance. Fuzzy Clustering algorithms partition methods that can be used to assign objects of dataset to their clusters. These algorithms optimize a subjective function that evaluates a given Fuzzy assignment of object to clusters. The problem of Fuzzy clustering is to find a fuzzy pseudo partition and the associated cluster centers, by which the structure of the data is represented as best as possible. To solve the fuzzy clustering problem, a performance index is formulated. Similar images are grouped by FCM; the group of images can be recognized within few minutes with better recognition. 6.3 APPLICATIONS OF FACE RECOGNITION Today, face recognition technology is being used to combat passport fraud, support law enforcement, identify missing children, and minimize benefit/identity fraud. The computer-based face recognition industry has made much useful advancement in the past decade. The proposed system of face recognition may be applied in identification systems,

11 147 document control and access control. The face similarity meter was found to perform satisfactorily in adverse conditions of exposure, illumination and contrast variations and face pose. Biometric technologies have found application in four broad application categories: surveillance, screening, enrollment identification and identity verification. General security tasks, such as access control to buildings, can be accomplished by a face recognition system. Banking operations and credit card transactions could also be verified by matching the image encoded in the magnetic strip of the card with the person using the card at any time. As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system. Face recognition can be used for both verification and identification (open-set and closed-set). Face recognition has received substantial attention from researches in biometrics, pattern recognition field and computer vision communities. The recognition of faces is very important because of its potential commercial applications, such as in the area of video surveillance, access control systems, retrieval of an identity from a data base for criminal investigations and user authentication.

12 148 The application of face recognition technique can be categorized into two main parts: law enforcement application and commercial application. major contribution. The following is a list of areas where face recognition can make a Law enforcement: Historically, the major user of biometrics, police agencies have used face recognition as a means of identifying criminals for well over a hundred years. Police gain the most benefit because a criminal's biometric information such as fingerprints, face recognition, DNA, etc, may already be held in a database. This enables forensic information collected at a crime scene and a witness description in the case of facial image, to be matched against it. Law enforcement agencies have achieved significant success with facial recognition, matching the mugshot (or even composite drawing) of a suspect against a database of offenders. This is particularly useful where the individual has refused to give his name, or has given a false name. Airport security: As in many cases, the only information available on suspected terrorists was a mug shot or surveillance photo and facial recognition was thrust to center stage as the biometric which could help identify them before they board the plane. While much work has been done in this area, the practical and logistical issues which have to be overcome have meant that so far implementation has not been as fast as originally anticipated. Child recovery: The proliferation of the Worldwide Web in the second half of the 90's had led to an explosion in the exchange of pedophilic imagery. Imaging and biometric technologies have achieved notable successes in tracking down pedophiles and identifying missing and abused children.

13 149 Access control: The use of fingerprints to verify an individual is well-established in biometrics. While a photo on a pass card helps prevent the use of the card by someone other than the individual to whom it was issued, it is far from infallible. The issue here is identity fraud, where the agency needs to know primarily three things: Before issuing a document, has a similar document already been issued to this individual under a different name? On subsequent presentation of the document, say at a Point of Entry, is the individual presenting it the same individual to whom it was issued? If not the same individual, who are they? Various biometrics can be used for these purposes. In 2003 the International Civil Aviation Organization (ICAO), stated a preference for fingerprints and facial recognition to be used on travel documents. Driver s Licenses: While driver s license is in many ways a similar application to ID cards, it is still the case in most countries that ID cards are not used and therefore the driver s license database is by far the largest database available of adult citizens. Drivers licenses are an ideal application for facial recognition because the individuals cooperate in having their photo taken, while the environment has been set up with optimal lighting, camera resolution, distance from the subject and neutral background. While this offers a potentially invaluable tool in identifying individuals whose details are in the database, because of privacy concerns it cannot be used for this purpose in certain jurisdictions. Smartcards: Smartcards are not a different application, but a particularly secure means of providing an individual with an identity card.

14 150 They are especially appropriate for biometrics because sufficient memory can be made available to hold the individual's facial image and a number of encode arrays. While these will always be held in a central database as well, having them on the card itself enables it to be used in locations where there may be no network access. Naturally, it is expected that the same techniques could be applied to identifying facial expressions where the set of training images is divided into classes based on the facial expression. Face recognition technology has numerous commercial and law enforcement applications. These applications range from static matching of controlled format photographs such as passports, credit cards, photo ID s, driver s licenses and mug shots to real time matching of surveillance video images. Understanding the human mechanisms employed to recognize a face constitutes a challenge for psychologists and neural scientists. In addition to the cognitive aspects, understanding face recognition is important, since the same underlying mechanisms could be used to build a system for the automatic identification of faces by machine. Machine recognition of faces is gradually becoming very important due to its wide range of commercial and law enforcement applications, which include forensic identification, access control, border surveillance and human computer interactions. Static matching of photographs (Mug-shot), Biometric security, Passport control at terminals in airports, Participant identification in meetings, Scanning for criminal persons, Video surveillance, Law enforcement (mug shot identification), Verification for personal identification (driver s licenses, passports, etc.), Electoral verification, Surveillance of crowd behavior are the various significant application of face recognition technology.

15 LIMITATIONS OF FACE RECOGNITION Face recognition is not so robust against extreme variations in expressions, pose and background. Also this cannot be used efficiently for faces with lateral head rotation. The computing power in general makes face recognition suitable for offline applications as online applications require high computing power. Face recognition alone as of now, cannot be successfully used for authentication but can be used for identification and classification. Identical twins cannot be distinguished using face recognition.