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A secure face spoof detection systems demands the capability to identify whether a face is from a real person or a spoofed image which is create by spoofer. Face spoofing induces deformation in the image and also degrades the image pattern quality. In this research work, analysis of distortion and the quality assessment of an image to identify spoof attack is the most important consideration. The existing methods in image distortion analysis, extracts the feature sets that capture the facial details but pattern of image is not in consideration. So, the designed spoof detection system utilizes a hybrid algorithm by combining the genetic algorithm and artificial neural network to create a unique feature sets according to the categories of database images. In addition, it also uses SIFT descriptor to extract the key points of face and identify the pattern of face in the ROI of image. Artificial Neural Network (ANN) classifier is used for the training of proposed spoof detection system. It is seen that the designed hybrid system face spoof detection achieves high performance than the existing system and execution time is also well. The proposed approach is evaluated using MATLAB simulator in image processing and computer vision toolbox.

Face Spoof Detection System Based on Genetic Algorithm and Artificial Intelligence Technique