THERMAL FACIAL PATTERN RECOGNITION FOR PERSONAL VERIFICATION USING FUZZY CMAC MODEL
Siu‐Yeung Cho, Chan Wai Ting, Chai Hiok Quek · 2011
This paper presents a novel personal verication system with thermal facial patterns using fuzzy neural network techniques. In contrast to traditional biometrics, the use of thermal technology removes two concerns in existing verication systems; they are: 1) the hygiene issue for verication systems that require physical contact (e.g., �ngerprints) and, 2) variation in ambient illumination for visible-band camera verica- tion system. In the proposed verication process, features extracted from thermal facial images are matched with trained fuzzy neural networks, in particular, the biologically in- spired TSK 0 -FCMAC, a fuzzy cerebellar model articulation controller (CMAC) based on the zero-ordered Takagi-Sugeno-Ka (TSK) fuzzy inference scheme. TSK 0 -FCMAC is capable of performing localized online training with an effective fuzzy inference scheme. Preliminary simulations show that the proposed verication system is able to achieve an Equal Error Rate (EER) of 6.1% and the True Acceptance Rate (TAR) of above 86% for identication. The work in this paper shows that the thermal face patterns can provide a reasonable level of discriminating power, and has the potential to be used in the con- text of biometric applications especially when used in conjunction with other biometric modalities.