Photogenic Expression Recognition using Gabor Filters and Support Vector Machines*

Luana Bezerra Batista, Herman Martins Gomes


Universidade Federal de Campina Grande
Departamento de Sistemas e Computação


Abstract. Facial Expression Recognition Systems (FERS) are usually applied to human-machine interfaces, enabling the utilization of services that require a good identification of the emotional state of the user. This work presents a new view of the facial expression recognition problem, by addressing the question of whether or not is possible to classify previously labeled photogenic and non-photogenic face images, based on their appearance. In the proposed approach, a Support Vector Machine (SVM) is trained with Gabor representations of the face images - extracted from their left-side - to learn the relationships between facial expressions and the concept of a good photography of the face of a person. The obtained results showed that Gabor filters and SVM are promising, having presented good recognition rates.

* This work was developed in collaboration with Hewlett-Packard.


[ contact authors ]

Proposed Approach


Examples of photogenic and non-photogenic pictures from Cohn-Kanade Database