Advanced Face Detection Algorithm for Arbitrary Rotation, Head-up, and Head-down Cases Using Prominent Facial Features and Hybrid Learning Techniques

• Main Authors: Chien-Yu Chen, 陳建宇
• Other Authors: 丁建均
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/a67sy8

碩士 === 國立臺灣大學 === 電信工程學研究所 === 106 === Face detection is one of the most research topics in the computer vision field and it also plays an important role on many applications of the face analysis algorithms, such as face recognition, age identification, facial expression recognition, and so on. It is the foundation of many applications. The final goal of face detection is given an arbitrary image, and to detect whether the face exists in this image or not. If the image contains the face, the position and range of the face in the image will be returned. Many people contribute to solving particular cases which cannot be detected easily because of pose variation, illumination variation, and occlusion. Therefore, we will provide the solutions on multi-view face detection in this thesis. In recent years, many researchers have intended to modify the well-known Viola and Jones (Adaboost) face detection algorithm. This Viola-Jones detector can be regarded as a milestone in the history of face detection. Nevertheless, its sufficient effectiveness is confined to frontal face detection. It is unable to get better detection rates on multi-view face detection. Hence, in this thesis, we propose a robust face detection algorithm based on the “Adaboost” machine learning algorithm and novel methods of facial features extraction to solve the multi-view face detection problems. First, we apply a skin-filter and Viola-Jones detector to conduct frontal face detection. Second, we extract the facial features of other face images which cannot be found the locations of the faces by first step through our proposed methods, e.g., mouth detection, nose detection, and ear detection. We make use of information of color, edge and contour to extract the facial features such as mouth and nose. Then, we propose a novel method based on the popular deep learning algorithm by improving the techniques of the Faster R-CNN to conduct ear detection. By these proposed methods of facial feature detection, we can obtain the locations of these prominent facial features and proceed to detect the correct locations of the profile faces which contain head-up and head-down cases. In addition, for some cases with head-raised, we apply the edge detection algorithm, morphological operations, and color information to detect eye and nostril candidates. By calculating the locations of the center points of eye and nostril candidates, we can obtain the correct locations of eye and nose to detect the face. Finally, we use the non-maximum suppression algorithm to improve the detection rates. We perform the proposed system on some popular multi-view face databases (e.g., FEI database, CVL database, Pointing’04 database, and so on). Our proposed methods can attain higher detection rates in this novel system, and the effectiveness will be demonstrated in this thesis.