Description

The Scale Invariant Feature Transform (SIFT) is an algorithm used to detect and describe scale-, translation- and rotation-invariant local features in images. The original SIFT algorithm has been successfully applied in general object detection and recognition tasks. One of its more recent uses also includes face recognition, where it was shown to deliver encouraging results. SIFT based face recognition techniques found in the literature rely heavily on the so-called key point detector, which locates interest points in the given image that are ultimately used to compute the SIFT descriptors. Automatic recognition of people is a challenging problem which has received much attention during recent years due to its many applications in different fields. Face recognition is one of those challenging problems and up to date, there is no technique that provides a robust solution to all situations. This paper presents a new technique for human face recognition. This technique uses an image-based approach towards artificial intelligence by removing redundant data from face images through image. The main advantage of this technique is its high-speed processing capability and low computational requirements, in terms of both speed and memory utilization. A face image database was created for the purpose of benchmarking the face recognition system. The image database is divided into two subsets, for separate training and testing purposes. During SOM training, 25 images were used, containing five subjects and each subject having 5 images with different facial expressions.