An Innovative Efficient and Light Weighted Approach to Face Recognition using SOM

Abstract

This paper presents a novel Self-Organizing Map (SOM) for face recognition. The SOM method is trained on images from one database. The novelty of this work comes from the integration of Images from input database, Training and Mapping with a single method of self organize map (SOM). The method proposed is less complex, light weighted and more accurate as compare to previous studies. Face Recognition has been identified as one of the attracting research areas and it has drawn the attention of many researchers due to its varying applications such as security systems, medical systems, entertainment, etc. Face recognition is the preferred mode of identification by humans: it is natural, robust and non-intrusive. Applications of face recognition include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. Face Recognition using unsupervised mode in neural network by SOM. Among the architectures and algorithms suggested for artificial neural network, the Self-Organizing Map has special property of effectively creating spatially organized „internal representation‟ of various features of input signals and their abstractions. After supervised fine tuning of its weight vectors, the Self-Organizing Map has been particularly successful in various pattern recognition tasks involving very noisy signal. So far all the face recognition systems were using combination of machine intelligent techniques for learning and classification which were making the system more complex and bulky.SOM has good feature extracting property due to its topological ordering. The Facial Analytics results for the database contain 213 images posed by 10 Japanese female models. Database reflects that the face recognition rate using one of the neural network algorithms, SOM is 98% for 10 Female.