Illumination-robust face recognition using ridge regressive bilinear models

The performance of face recognition is greatly affected by illumination changes because intra-person variation of the captured images under different lighting conditions can be much bigger than the inter-person variation. This paper proposes an illumination-robust face recognition by separating an identity factor and an illumination factor using symmetric bilinear models. The translation procedure in the bilinear model requires a repetitive computation of matrix inverse operations to reach the identity and illumination factors. This computation may result in a non-convergent case when the observation has noisy information or the model is overfitted. To alleviate this situation, we suggest a ridge regressive bilinear model that combines the ridge regression into the bilinear model. This provides a number of advantages: it stabilizes the bilinear model by shrinking the range of identity and illumination factors appropriately and improves the recognition performance. Experimental results...

• Type: Journal
• Year: 2008
• Where: PRL
• Authors: Dongsoo Shin, Hyung-Soo Lee, Daijin Kim
• Keywords: Bilinear Model, Illumination Factors, Model, PRL 2006, PRL 2008