Erina Takikawa

Person-Specific SIFT Features for Face Recognition

Scale invariant feature transform (SIFT) proposed by Lowe has been widely and successfully applied to object detection and recognition. However, the representation ability of SIFT features in face recognition has rarely been investigated systematically. In this paper, we proposed to use the person-specific SIFT features and a simple non-statistical matching strategy combined with local and global similarity on key-points clusters to solve face recognition problems. Large scale experiments on FERET and CAS-PEAL face databases using only one training sample per person have been carried out to compare it with other non person-specific features such as Gabor wavelet feature and local binary pattern feature. The experimental results demonstrate the robustness of SIFT features to expression, accessory and pose variations.

Ethnicity estimation with facial images

We have advanced an effort to develop vision based human understanding technologies for realizing human-friendly machine interfaces. Visual information, such as gender, age ethnicity, and facial expression play an important role in face-to-face communication. This paper addresses a novel approach for ethnicity classification with facial images. In this approach, the Gabor wavelets transformation and retina sampling are combined to extract key facial features, and support vector machines that are used for ethnicity classification. Our system, based on this approach, has achieved approximately 94% for ethnicity estimation under various lighting conditions.

Discriminant analysis in correlation similarity measure space

Correlation is one of the most widely used similarity measures in machine learning like Euclidean and Mahalanobis distances. However, compared with proposed numerous discriminant learning algorithms in distance metric space, only a very little work has been conducted on this topic using correlation similarity measure. In this paper, we propose a novel discriminant learning algorithm in correlation measure space, Correlation Discriminant Analysis (CDA). In this framework, based on the definitions of within-class correlation and between-class correlation, the optimum transformation can be sought for to maximize the difference between them, which is in accordance with good classification performance empirically. Under different cases of the transformation, different implementations of the algorithm are given. Extensive empirical evaluations of CDA demonstrate its advantage over alternative methods.

Gender and age estimation system robust to pose variations

For applications based on facial image processing, pose variation is a difficult problem. In this paper, we propose a gender and age estimation system that is robust against pose variations. The acceptable facial pose range is a yaw (left-right) from -30 degrees to +30 degrees and a pitch (up-down) from -20 degrees to +20 degrees. According to our experiments on several large databases collected under real environments, the gender estimation accuracy is 84.8% and the age estimation accuracy is 80.9% (subjects are divided into 5 classes). The average processing time is about 70 ms/frame for gender estimation and 95 ms/frame for age estimation (Pentium4 3.2 GHz). The system can be used to automatically analyze shopping customers and pedestrians using surveillance cameras.