Recent Advances in Face Recognition

The main idea and the driver of further research in the area of face recognition are security applications and human-computer interaction. Face recognition represents an intuitive and non-intrusive method of recognizing people and this is why it became one of three identification methods used in e-passports and a biometric of choice for many other security applications. This goal of this book is to provide the reader with the most up to date research performed in automatic face recognition. The chapters presented use innovative approaches to deal with a wide variety of unsolved issues. Multi layer neural networks (MLNNs) have proved to be among the best existing techniques used in automatic face recognition due to their inherent robustness and generalizing ability. There are many papers in the literature that suggest different approaches in using neural networks to achieve better performance. The focus so far has been on studying different training algorithms and different feature extraction techniques. Yet, the majority of the work done in all face recognition methods in general and in methods that use neural networks specifically is based on grayscale face images. Until recent studies demonstrated that color information makes contribution and enhances robustness in face recognition. The common belief was the contrary. Grayscale images are used to save storage and processing costs. Colored images are usually composed of three components (Red, Green, Blue) while grayscale images are composed of one component only which is some form of averaging of the three color components, thus it basically requires one third of the cost. However, grayscale images only tell part of the story, and even though the enhancement that color adds might not seem very important to systems that use face recognition for basic identification applications, colors will be crucial for more advanced future applications where higher levels of abstraction is needed. There are many growing areas of computer vision in applications such as robotics, intelligent user interfaces, authentication in security systems and face search in video databases that are demanding color information important for future progress. This article illustrates the importance of using color information in face recognition and introduces a new method for using color information in techniques based on MLNNs without adding mentionable processing cost. This method involves the new network architecture, and it can be used to enhance the performance of systems based on MLNNs regardless of the training algorithm or feature extraction technique. Motivated by how each pixel in a picture is comprised of its own unique color and the relation among those color components, we have proposed a new architecture. The new proposed network architecture involves the neurons in the input layer to be divided into three groups, each of which is connected to a separate input vector that represents one of the three color channels (Red, Green, Blue). This way, the modification allows us to make a full use of color information without extra processing costs. In addition, even though the input of the three color channels is larger than the standard input for the grayscale image, there is still the same number of connections, resulting in no further processing cost than the standard MLP. Experimental results that compare the performance of different approaches with and without using the proposed approach are demonstrated. Based on the aforementioned theoretical analysis and experimental results, the superiority of the proposed approach in face recognition is claimed, where the color distribution is determined by iteratively updating the weights that correspond to each color channel for the specific pictures in concern In this chapter, we proposed a new face image acquisition system and multi-views face database. Face recognition using PCA and ICA were discussed. We evaluated the performance of ICA according to the recognition rate on this new multi-views face database. We explored the issues of subspace selection, algorithm comparison, and multi-views performance. We also proposed a strategy in order to improve the recognition performance, which performs the majority-voting using five views of each face. Our results are, in Preface&Contents_Face_Recognition......Page 0 Preface&Contents_Face_Recognition......Page 1 01_Delac......Page 11 02_Fui......Page 25 03_Ginhac......Page 37 04_Hua......Page 49 05_Khashman......Page 65 06_Li......Page 81 07_Majumdur......Page 89 08_Nishiyama......Page 107 09_Pan......Page 119 10_Rama......Page 135 11_Singh......Page 159 12_Tsai......Page 171 13_Wu......Page 193 14_Yang......Page 217 15_Youssef......Page 233