Metodologia para a captura, detecção e normalização de imagens faciais

Abstract

With the evolution of information technology Facial recognition is becoming a common task. This artifact can be used in security, controlling access to restricted places and identifying persons, for example. Facial recognition is a complex task, and it's divided into some process, comprising: facial images capture, detection of regions of interest, facial normalization, feature extraction and recognition itself. Among these, the first three are treated in this work, which has as its main objective the automatic normalization of faces. For the capture of images and for the image normalization there are international standards that standardize the procedure for implementing these tasks and which were used in this work. In addition to following these rules, other standardizations have been developed to build a database of facial images in order to assist the process of face recognition. A new methodology for normalization of profile faces, based on the rules of frontal normalization. Some ways of normalization were implemented: frontal semiautomatic, lateral semiautomatic and automatic frontal. For the execution of frontal automatic normalization we need two points of interest, the two eyes, which makes it a necessary step to execute the detection regions of interest. In this work, we compared two similar methods for detecting. Where was first detected a region containing both eyes and then, within this region were detected each eye more accurately. For the two methodologies were used techniques of image processing and pattern recognition. The first method based on the Viola and Jones algorithm, the filter uses as Haar-like Features with the technique of pattern recognition Adaptive Boosting. Where the second algorithm equivalent techniques were Local Binary Pattern and Support Vector Machines, respectively. In the second algorithm was also used an optimization algorithm based on neighborhood search, the Variable Neighborhood Search. This studies resulted in a database with 3726 images, a frontal normalized database with 966 images and a database with face's profile normalized with 276 images. The eye detection resulted in better tests, about 99 % accuracy for the first method and 95 % for the second, and in all tests the first algorithm was the fastest. With the development of future work we have: make public the images database, improve the percentage of accuracy and processing speed for all tests and improve the normalization by implementing the normalization of the background and also lighting.