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|Title:||A real-time multiple human face detection and recognition based on scalable vocabulary tree||Authors:||Yang, Wen||Keywords:||DRNTU::Engineering||Issue Date:||2013||Abstract:||Human face detection and recognition have many applications such as camera surveillance, biometric applications, and human computer interface. Automatic human face detection and recognition from images is a challenging task due to the uncertainty in cluttered background, view point, illumination, occlusion, and facial aging. An e cient implementation of real time face detection has been done. Face detection is based on sophisticated lters. The detection accuracy of the state-of-the-art object detection algorithms is rather high. However, the computational cost is also high. In this work, the face lter is trained o ine from the available source code of general object detecion algorithm. The main idea of the algorithm is object detection based on mixtures of multiscale deformable part models. Once the face lter is trained, it is used in the C code for real-time video surveillance system. Using this system, webcam can capture face of humans in realtime. The face detection accuracy is tested to be over 90% in 2 seconds. In contrast, using the original MATLAB code, the detection accuracy is the same while the detection time ranges from 15s to 30s. An e ective algorithm for real time face recognition has been applied. According to comprehensive survey of the recognition methods, it is found that Scalable Vocabulary Tree (SVT) is e ective in general image recognition. As compared to general scene images, which contain generic topics such as outdoor, o ce, and sky, face images usually contain speci c face parts such as eye, nose and mouth. The implementation is carried out in MATLAB since the algorithm is rather e cient. The face detection accuracy is also roughly 90% in less than 0.5s.In addition, new face images have been collected. In order to test the performance of face detection, the author has been using the webcam to capture her face based on the developed software since half year ago. The capturing conditions varies, including di erent lightings, angles, occlusions, makeups, hairstyles, glasses, etc. Then the detected faces are recognized using the face image databases.||URI:||http://hdl.handle.net/10356/55118||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
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