Um sistema para detecção e reconhecimento de face em vídeo utilizando a transformada cosseno discreta

• Autor(a) principal: Omaia, Derzu
• Data de Publicação: 2009
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFPB
• Texto Completo: https://repositorio.ufpb.br/jspui/handle/tede/6107
• topic: Detecção de Face; Reconhecimento de Face; Reconhecimento de Padrões; Processamento Digital de Imagens; Transformada Cosseno Discreta; Face Detection; Face Recognition; Pattern Recognition; Digital Image Processing; Discrete Cosine Transform; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

Human face has a very complex and variable pattern, which makes the face detection and recognition operations a challenging problem. The scope of these operations is quite comprehensive, involving mainly security applications, such as authorization for physical and logical access, people tracking, and real time authentication. In addition to security applications, face detection and recognition can also be associated with other applications, such as human-computer interaction and virtual reality. Several studies of face detection and recognition have been proposed and developed by researchers, pursuing greater precision and efficiency. Currently there are face detectors and recognizers with accuracy exceeding 95%. Commercial systems are available as well. This work presents a study on several face detection and recognition methods. Also was discussed the possibility of developing a new face detection method using Prediction by Partial Match (PPM), Entropy and Discrete Cosine Transform (DCT). It is further proposed a new face recognition method based on DCT. Finally, is proposed an architecture for a face detection and recognition system in video. To validate the architecture, the proposed system was implemented using one of the best detectors in the literature and the recognizer produced in this work. Several experiments were performed, and both the face detector used as the recognizer developed were effective, achieving success rates compatible with most current methods