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Face Differentiation Algorithm

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Comparative Study on Differentiating Identical Twins by Face Recognition Algorithm R. Prema Dr. P. Shanmugapriya Research Scholar and Assistant Professor Associate Professor Dept. of CSE Dept. of IT SCSVMV University SCSVMV University Kanchipuram,Tamil Nadu, India Kanchipuram ,Tamil Nadu, India. Premarajan2013@gmail.com ABSTRACT: Face recognition presents a challenging problem in the field of computer vision and image processing. Such has received a great deal of attention over the last few years because of its many applications in various platforms. Many researches in face recognition have been dealing with the challenge of the great variability in head pose, lighting intensity and direction, facial expression, …show more content…

A color progression based interest operator (the fast radial symmetry transform) is applied to the masked image. The transform detects regions of high radial symmetry. Applying a threshold to the output of the fast radial symmetry transform result in detecting bright or dark regions of high radial symmetry, which corresponds to potential facial marks. D. Bipartite graph matching: The process for matching facial marks detected by the multi-scale automatic facial mark detector is similar.In the case of automatically detected facial marks, each facial mark is characterized only by its geometric location on the corresponding face image. Therefore, automatically detected facial marks are treated as point features and can be viewed as they all belong to the same category. The similarity in the distribution of facial marks is used to determine the similarity between two face images. The similarity is computed by formulating a bipartite graph matching …show more content…

The technique presented in this paper clearly identifies different faces, twins and same face. A multi-scale automatic facial mark detection system for distinguishing between identical twins solely based on the geometric distribution of facial marks was proposed.When comparing two algorithms experimental results for multi-scale automatic facial mark detector show that the performance of the technique presented in the paper is very good to distinguish between the twins. If the method is modified by considering large number of data samples and use genetic algorithm or neural network algorithms, the performance can be improved. In the future, we will explore using richer facial mark characteristics like texture, shape and color to improve performance. We hope to further explore the use of different matching algorithms and compare it to the proposed matching algorithm. Facial marks features can be fused with other facial features to enrich facial characterizations for improved performance. The results of the investigation makes a case for the use of facial marks in biometric

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