AN ADAPTIVE FUZZY FUSION FRAMEWORK FOR FACE RECOGNITION UNDER ILLUMINATION VARIATION BASED ON LOCAL MULTIPLE PATTERNS

作者：Zhou Lifang; Fang Bin^*; Li Weisheng; Chen Hengxin; Wang Lidou

来源：International Journal of Pattern Recognition and Artificial Intelligence, 2013, 27(1): 1356002.

DOI：10.1142/S0218001413560028

• Face recognition
• local multiple patterns (LMP)
• entropy weight
• fuzzy fusion framework

Local binary pattern (LBP) operator offers an efficient way to recognize face under varying illumination, while it has the drawback of abandoning some important texture features. Local multiple patterns (LMP) has alleviated the problem by a hierarchical model. However, the LMP method can bring out the rapid expansion of feature dimension, so a special feature encoding method is adopted by this paper. Meanwhile, we find that the LMP features of different layers can be used to recognize face independently so that it would preserve more abundant recognition information. Most importantly, the contribution of the LMP features from different layers is blurry under varying illumination. We propose a fuzzy framework to fuse the recognition result of different layers and use adaptive weights to calculate contribution rates of different layers under varying illumination. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods on four databases such as Yale B, Extended Yale B, CMU PIE and Outdoor.

• 出版日期2013-2
• 单位重庆大学