Face recognition in different subspaces - A comparative study

Face recognition is one of the most successful applications of image analysis and understanding and has gained much attention in recent years. Among many approaches to the problem of face recognition, appearance-based subspace analysis still gives the most promising results. In this paper we study the three most popular appearance-based face recognition projection methods (PCA, LDA and ICA). All methods are tested in equal working conditions regarding preprocessing and algorithm implementation on the FERET data set with its standard tests. We also compare the ICA method with its whitening preprocess and find out that there is no significant difference between them. When we compare different projection with different metrics we found out that the LDA+COS combination is the most promising for all tasks. The L1 metric gives the best results in combination with PCA and ICA1, and COS is superior to any other metric when used with LDA and ICA2. Our results are compared to other studies and some discrepancies are pointed ou

A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method

In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCA-KNN) approach. The proposed method consists of three parts. The first part is based on preprocessing face images using Graph Based algorithm and SIFT (Scale Invariant Feature Transform) descriptor. Graph Based topology is used for matching two face images. In the second part eigen values and eigen vectors are extracted from each input face images. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. In the final part a nearest neighbor classifier is designed for classifying the face images based on the SPCA-KNN algorithm. The algorithm has been tested on 100 different subjects (15 images for each class). The experimental result shows that the proposed method has a positive effect on overall face recognition performance and outperforms other examined methods

Towards Practical Face Recognition System Employing Row-Based Distance Method In 2dpca Based Algorithms

Automatic face recognition has been a focus research topic in past few decades. This is due to the advantages of face recognition and the potential need for high security in commercial and law enforcement applications. However, due to nature of the face, it is subjected to several variations. Thus, finding a good face recognition system is still an active research field till today. Many approaches have been proposed to overcome the face variations. In the midst of these techniques, subspace methods are considered the most popular and powerful techniques. Among them, eigenface or Principal Component Analysis (PCA) method is considered as one of the most successful techniques in subspace methods. One of the most important extensions of PCA is Two-dimensional PCA (2DPCA). However, 2DPCA-based features are matrices rather than vectors as in PCA. Hence, different distance computation methods have been proposed to calculate the distance between the test feature matrix and the training feature matrices. All previous methods deal with the classification problem mathematically without any consideration between feature matrices and the face images. Besides, the system performance in practical applications relies on the number of eigenvectors chosen. As a solution to the above mentioned issues, four new distance methods have been proposed in this thesis, which are based on the rows of a feature matrix of 2DPCA-based algorithms. Through experiments, using eight face databases, their improvements compared to the previous distance methods are demonstrated

Face Recognition Using Eigen-Wavelet-Face Method

This work is concerned with investigation for face recognition methods suitable for different environments.  Eigenface method based on Principle Component Analysis (PCA) is modified here by operating on wavelet transformed face image to extract recognition features in a hybrid scheme called Eigen-Wavelet-Face aiming to improve the recognition rate and/or complexity.  Four standard face image databases are used in the work. The databases have different parameters related to size, type, expressions, lighting, orientation, and the number of images per person. The original Eigenface and suggested Discrete Wavelet Transform (DWT) face recognition methods are also used in the work for the sake of comparison. The results showed that the Eigenface method is a time consuming due to its huge computations.  For databases having large number of training images and variations, the proposed hybrid method achieved 100% recognition rate, while for those databases with smaller training sets DWT method obtained the best recognition rate of 95% under favorite condition. Key words: Face recognition, Eigenface, PCA, DWT, Feature extraction.

Swarm Optmization Algorithms for Face Recognition

In this thesis, a face recognition system based on swarm intelligence is developed. Swarm intelligence can be defined as the collective intelligence that emerges from a group of simple entities; these agents enter into interactions, sense and change their environment locally. A typical system for face recognition consists of three stages: feature extraction, feature selection and classification. Two approaches are explored. First, Bacterial Foraging Optimization(BFO), in which the features extracted from Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA) are optimized. Second, Particle Swarm Optimization(PSO), which optimizes the transform coefficients obtained from the Discrete Cosine Transform(DCT) of the images. PCA, LDA and DCT are all appearance-based methods of feature extraction. PCA and LDA are based on global appearance whereas DCT is performed on a block by block basis exploring the local appearance-based features. Finally, for classification Euclidean distance metric is used. The algorithms that have been applied are tested on Yale Face Database

Face Image Retrieval in Image Processing – A Survey

The task of face recognition has been actively researched in recent years. Face recognition has been a challenging and interesting area in real time applications. With the exponentially growing images, large-scale content-based face image retrieval is an enabling technology for many emerging applications. A large number of face recognition algorithms have been developed in last decades. In this paper an attempt is made to review a wide range of methods used for face recognition comprehensively. Here first we present an overview of face recognition and discuss the methodology and its functioning. Thereafter we represent the most recent face recognition techniques listing their advantages and disadvantages. Some techniques specified here also improve the efficiency of face recognition under various illumination and expression condition of face images This include PCA, LDA, SVM, Gabor wavelet soft computing tool like ANN for recognition and various hybrid combination of these techniques. This review investigates all these methods with parameters that challenges face recognition like illumination, pose variation, facial expressions. This paper also focuses on related work done in the area of face image retrieval