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In this paper, we propose a simple, but efficient method to recognize two-dimensional shapes without regard to their translation, rotation, and scaling factors. In our scheme, we use all of the boundary points to calculate the first principal component, which is the first shape feature. Next, by dividing the boundary points into groups by projecting them onto the first principal component, each shape is partitioned into several blocks. These blocks are processed separately to produce the remaining shape features. In shape matching, we compare two shapes by calculating the difference between the two sets of features to see whether the two shapes are similar or not.

The amount of storage used to represent a shape in our method is fixed, unlike most other shape recognition schemes. The time complexity of our shape matching algorithm is also O(n), where n is the number of blocks. Therefore, the matching algorithm takes little computation time, and is independent of translation, rotation, and scaling of shapes.

Automatic Face Recognition (FR) presents a challenging task in the field of pattern recognition and despite the huge research in the past several decades; it still remains an open research problem. This is primarily due to the variability in the facial images, such as non-uniform illuminations, low resolution, occlusion, and/or variation in poses. Due to its non-intrusive nature, the FR is an attractive biometric modality and has gained a lot of attention in the biometric research community. Driven by the enormous number of potential application domains, many algorithms have been proposed for the FR. This paper presents an overview of the state-of-the-art FR algorithms, focusing their performances on publicly available databases. We highlight the conditions of the image databases with regard to the recognition rate of each approach. This is useful as a quick research overview and for practitioners as well to choose an algorithm for their specified FR application. To provide a comprehensive survey, the paper divides the FR algorithms into three categories: (1) intensity-based, (2) video-based, and (3) 3D based FR algorithms. In each category, the most commonly used algorithms and their performance is reported on standard face databases and a brief critical discussion is carried out.