Facial detection and recognition system using hybrid techniques in images and video sequences
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Abstract
Recognizing the identity of an individual automatically is still a task that fails to achieve a success rate of 100%, so the present work sought to improve the recognition rate; giving emphasis to the methods of extraction and classification of characteristics. In this sense, it was proposed to improve the ratio for the recognition of faces through the representation of the images, using the Wavelets transform of Gabor on the grayscale images obtained after normalizing the original images, afterwards the new representation obtained was applies the Principal Component Analysis (PCA) technique to obtain and then form the feature vector of face images. Next, a classifier based on Vector Support Machines (SVM) is applied. The method was tested on a database of images of faces constituted between the banks of faces FERET, ORL and images obtained by those responsible for the investigation. As a result of the combination of Gabor Wavelet Transform and Principal Components Analysis techniques in the process of extracting features and image classification based on Vector Support Machines, a recognition rate higher than 95% is achieved.
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