Abstract
Face recognition is one of the most unobtrusive biometric techniques that can be used for access control as well as surveillance purposes. Various methods for implementing face recognition have been proposed with varying degrees of performance in different scenarios. The most common issue with effective facial biometric systems is high susceptibility of variations in the face owing to different factors like changes in pose, varying illumination, different expression, presence of outliers, noise etc. This paper explores a novel technique for face recognition by performing classification of the face images using unsupervised learning approach through K-Medoids clustering. Partitioning Around Medoids algorithm (PAM) has been used for performing K-Medoids clustering of the data. The results are suggestive of increased robustness to noise and outliers in comparison to other clustering methods. Therefore the technique can also be used to increase the overall robustness of a face recognition system and thereby increase its invariance and make it a reliably usable biometric modality.
Highlights
Face is a natural mode of identification and recognition in humans
The method discussed in this paper describes a novel approach of K-Medoids clustering [21] for face recognition
After performing processing and feature extraction of the face images in the data set, K-Medoid clustering using Partitioning Around Medoids (PAM) [21] was done over the data space D which represented the feature information from n face images
Summary
Face is a natural mode of identification and recognition in humans. It comes intuitively to people for recognising others. One of the most useful advantages of having face as a morphological trait for recognition purpose was its non invasiveness. It was beneficial both in terms of cost, time and efforts to record the data for the biometric system. It altogether removed the need of having expensive scanners which were vital for other biometric systems like fingerprint, iris etc. It could be used even without the knowledge of the user and immediately found its application in surveillance
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