Studies on Some Statistical Techniques for Human Face Recognition

By Arindam Kar

The language of the image is universal. Images were the means of communicating information in ancient days. Even today, though people from different parts of the world speak in different languages, an image conveys almost the same universal meaning to all. With the key advancement in modern computer technologies, the quality of communication of information using image cannot be ignored. 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. Face recognition is primarily a high-dimensional pattern recognition problem. Research in this area has been conducted for more than 30 years; as a result, the current status of face recognition technology is well advanced. Even low-resolution face images generate huge dimensional feature spaces (20000 dimensions in the case of a ሺͳͲͲ ൈ ʹͲͲሻ pixels face image). In addition to the problems of large computational complexity and memory storage, this high dimensionality makes very difficult to obtain statistical models of the input space using well-defined parametric models. Moreover, this last aspect is further stressed given the fact that only few samples for each class ሺͳȂ ͵ሻ are usually available for the system training. Face recognition is one of the biometric methods for identifying individuals by the features of the face. Facial recognition is an important ability of human beings in daily life. One can remember hundreds or even thousands of faces in our whole life and recognize a face in different perspective variations, illuminations, ages, etc. Under very poor illumination conditions, the face can still be recognized, as the state of the different facial features and the face contours are usually sufficient for recognition. This suggests an approach to face recognition, whereby the geometrical positions of the different facial features are measured first, and then the details of each feature are used for further matching. This is also a common approach for object recognition. Thus Face recognition involves computer recognition of personal identity based on geometric or statistical features derived from face images 1, 2]. A significant interest in the area of face recognition is to develop an automatic face recognition system, which is robust against variance in pose, illumination, expression and occlusion, at the same time; the system has to take the computational cost into consideration for real time applications.

• Format: Paperback
• Language: English
• ISBN: 8196431503
• ISBN13: 9788196431501
• Release Date: July 2023
• Publisher: Arindam Kar
• Length: 198 Pages
• Weight: 0.60 lbs.
• Dimensions: 0.4" x 6.0" x 9.0"