Details

Autor(en) / Beteiligte

• Omara, Ibrahim
• Xiaohe Wu
• Hongzhi Zhang
• Yong Du
• Wangmeng Zuo

Titel

Learning pairwise SVM on deep features for ear recognition

Ist Teil von

• 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), 2017, p.341-346

Ort / Verlag

IEEE

Erscheinungsjahr

2017

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• Recently, deep features extracted from Convolutional Neural Networks (CNNs) have been widely adopted in various applications, such as face recognition. Compared with the handcrafted descriptors, deep features have more powerful representation ability which can lead to better performance. Effective feature representations play an important role in ear recognition. While deep features have not been applied to represent the ear images. In this paper, we propose to extract deep features of ear images based on VGG-M Net for solving the ear recognition problem. And due to the lack of training images per person, we propose to use the pairwise SVM for classification firstly. For computational efficiency, Principal Component Analysis (PCA) is exploited to reduce the dimension before classification. Finally, we evaluate our approach on two public ear databases: USTB I and USTB II. The experimental results achieve a promising recognition rate and show superior performance compared with the state-of-the-art methods.