Facial Attribute Detection by Deep Neural Network

• Main Authors: Jia-Shin Lan, 藍家馨
• Other Authors: Winston Hsu
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/27757791944778213699

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 104 === Facial attributes have gained popularity in the past few years in machine vision tasks including recognition, classification, and retrieval. Predicting facial attributes from web images is very challenging due to background clutters and face variations, such as scale, pose, and illumination in the real world. The key to this problem is to build proper feature representations to cope with these unfavourable conditions. Given the success of deep neural network (DNN) in image classification, the high level DNN feature as an intuitive and reasonable choice has been widely utilized for this problem. DNN is powerful to handle face variation, but it needs heavy computation efforts and memory storage resources. Improving the accuracy of attribute classifiers is an important first step in any application which uses these attributes. Therefore, our goal is improving face attribute detection performance with smaller architecture of deep models. Our network is pre-trained with massive face identities, then fine-tuned with attribute labels. We consider the DNN features as face representation for attribute prediction. We demonstrate the effectiveness of our method by producing results on the challenging publicly available datase CelebA.