05-07-2013, 04:23 PM
Improved Face Recognition Approaches for the Identification Purposes
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INTRODUCTION
Face recognition is an easy task for humans. Experiments in [1] have shown, that even one to three day old babies are able to distinguish between known faces. So anyone can think the difficulty for a computer. It turns out we know little about human recognition to date. Inner features such as: eyes, nose, mouth or outer features such as: head shape, hairline can be used for a successful face recognition is doubtful. How do we analyze an image and how does the brain encode it? It was shown by David Hubel and Torsten Wiesel, that our brain has specialized nerve cells responding to specific local features of a scene, such as lines, edges, angles or movement. Since we don't see the world as scattered pieces, our visual cortex must somehow combine the different sources of information into useful patterns. Automatic face recognition is all about extracting those meaningful features from an image, putting them into a useful representation and performing some kind of classification on them.
Face recognition based on the geometric features of a face is probably the most intuitive approach to face recognition. One of the first automated face recognition systems was described in [2]; marker points such as: position of eyes, ears and nose were used to build a feature vector (distance between the points, angle between them, ...). The recognition was performed by calculating the euclidean distance between feature vectors of a probe and reference image. Such a method is robust against changes in illumination by its nature, but has a huge drawback: the accurate registration of the marker points is complicated, even with state of the art algorithms. Some of the latest work on geometric face recognition was carried out in [3]. A 22-dimensional feature vector was used and experiments on large datasets have shown, that geometrical features alone don't carry enough information for face recognition.
The Eigenfaces method described in [4] took a holistic approach to face recognition: A facial image is a point from a high-dimensional image space and a lower-dimensional representation is found, where classification becomes easy. The lower-dimensional subspace is found with Principal Component Analysis, which identifies the axes with maximum variance. While this kind of transformation is optimal from a reconstruction standpoint, it doesn't take any class labels into account. Imagine a situation where the variance is generated from external sources, let it be light. The axes with maximum variance do not necessarily contain any discriminative information at all, hence a classification becomes impossible. So a class-specific projection with a Linear Discriminant Analysis was applied to face recognition in [5]. The basic idea is to minimize the variance within a class, while maximizing the variance between the classes at the same time.
Recently various methods for a local feature extraction emerged. To avoid the high-dimensionality of the input data only local regions of an image are described, the extracted features are (hopefully) more robust against partial occlusion, illumation and small sample size. Algorithms used for a local feature extraction are Gabor Wavelets [6], Discrete Cosinus Transform [7] and Local Binary Patterns [8, 9, 10]. It's still an open research question how to preserve spatial information when applying a local feature extraction, because spatial information is potentially useful information.
The task of recognizing faces has attracted much attention both from neuroscientists and from computer vision scientists. Various approaches for 2D face recognition have been proposed in the literature, which can be classified into three categories: analytic (feature based), holistic (global) and hybrid methods. While analytic approaches compare the salient facial features or components detected from the face, holistic approaches make use of the information derived from the whole face pattern. By combining both local and global features, hybrid methods attempt to produce a more complete representation of facial images.
Machine recognition of faces is emerging as an active research area spanning several disciplines such as image processing, pattern recognition, computer vision and neural networks. 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- license's, and mug shots to real time matching of surveillance video images.
Understanding the human mechanisms employed to recognize faces 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. Although, humans seem in recognize faces in cluttered scenes with relative having the ability to identify distorted images, coarsely quantized images, and faces with occluded details, machine recognition is much more daunting task.
A formal method of classifying Faces has been first proposed by Francis Galton. Research interest in face recognition has grown significantly in recent years as a result of the following facts: 1) The increase in emphasis on civilian/commercial research projects, 2) The increasing need for surveillance related applications due to drug trafficking, terrorist activities, etc. 3) The re-emergence of neural network classifiers with emphasis on real time computation and adaptation, 4) The availability of real time hardware.
The human face plays an important role in our social interaction, conveying people’s identity. Using the human face as a key to security, biometric face recognition technology has received significant attention in the past several years due to its potential for a wide variety of applications in both law enforcement and non-law enforcement. As compared with other biometrics systems using flngerprint/palmprint and iris, face recognition has distinct advantages because of its non-contact process. Face images can be captured from a distance without touching the person being identified, and the identification does not require interacting with the person. In addition, face recognition serves the crime deterrent purpose because face images that have been recorded and archived can later help identify a person.