Camera Independent Face Recognition Algorithm In Visual Surveillance

Face recognition in visual surveillance has the ability to reduce crime rates in public area due to the suspect’s identity can be automatically identified in real-time using the face images captured by the surveillance camera as circumstantial evidence. Several available image preprocessing techniques, classifiers, and approaches had been proposed and tested to mitigate the effect of illumination variation, pose variations, and intensity quality differences due to hardware differences in such system. The face recognition system should be able to integrate seamlessly into the existing system. From the experiments, Histogram Equalization (HE) preprocessed face images scaled to 30�30 had proven to be well suited for pre-processing of surveillance images. The combination of Linear Discriminant Analysis (LDA) and HE preprocessed images managed to achieve an average recognition rate of 81.48% for the single camera training set. The flandmark facial landmark detector is implemented to determine the location of the eyes and new face images are obtained by cropping the HE pre-processed images. The combination of flandmark images at 20�30 with multi-class Support Vector Machine (SVM) is used to form a multimodal classification system with LDA and HE combination. Score level fusion is done to the normalized output scores of both the classifiers with proper weight, w assigned to each score. Finally, the watch list principle will list out several possible subjects according to their respective score ranking rather than deciding on a particular subject based on the maximum score, thus increasing the performance of the proposed system. The experimental results demonstrate the performance of the proposed algorithm on Surveillance Camera Face Database (SCface) database with 97.45% average recognition rate

A comparative analysis of neural and statistical classifiers for dimensionality reduction-based face recognition systems.

Human face recognition has received a wide range of attention since 1990s. Recent approaches focus on a combination of dimensionality reduction-based feature extraction algorithms and various types of classifiers. This thesis provides an in depth comparative analysis of neural and statistical classifiers by combining them with existing dimensionality reduction-based algorithms. A set of unified face recognition systems were established for evaluating alternate combinations in terms of recognition performance, processing time, and conditions to achieve certain performance levels. A preprocessing system and four dimensionality reduction-based methods based on Principal Component Analysis (PCA), Two-dimensional PCA, Fisher\u27s Linear Discriminant and Laplacianfaces were utilized and implemented. Classification was achieved by using various types of classifiers including Euclidean Distance, MLP neural network, K-nearest-neighborhood classifier and Fuzzy K-Nearest Neighbor classifier. The statistical model is relatively simple and requires less computation complexity and storage. Experimental results were shown after the algorithms were tested on two databases of known individuals, Yale and AR database. After comparing these algorithms in every aspect, the results of the simulations showed that considering recognition rates, generalization ability, classification performance, the power of noise immunity and processing time, the best results were obtained with the Laplacianfaces, using either Fuzzy K-NN.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .X86. Source: Masters Abstracts International, Volume: 45-01, page: 0428. Thesis (M.A.Sc.)--University of Windsor (Canada), 2006

Development Of Famous People Recognition System From Video Sequences

Video based face recognition has become an important task due to the huge demand on the surveillance system application such as monitoring activities of closed circuit TV (CCTV). There are some cases due to the security reason, an identity of interest (IoI) need to be searched manually from all the captured video through CCTV devices. This task is tiring, tedious and wasting time by looking through video one by one. Therefore, the initial work of developing a basic system for video based face recognition on searching selected identity of interest automatically is proposed in this research. In this research, combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used as feature extractor. Three feature classifiers used in this system are Euclidean Distance, Manhattan Distance, and Learning Vector Quantization Neural Network (LVQNET). Comparison between the performance of three classifiers have been conducted on the overall recognition results of selected video for famous people recognition. Experimental results of the proposed method on selected video has the recognition accuracy up to 76.4% for Euclidean distance, 75.9% for Manhattan distance,and 64.5% for LVQNET Network with histogram equalization filtering technique is applied. The ability of the proposed system has been proven to be effective and has a significant value for intelligent applications such as automated video based face recognition on selected person