Selective generation of Gabor features for fast face recognition on mobile devices

• We propose a robust face recognition method for fast response on a mobile device.
• The Gabor features are selectively generated by using a contribution measure.
• Face recognition is performed using only the selectively generated Gabor features.
• The execution time of feature extraction can be reduced by more than 50%.

In this paper, we propose a robust face recognition method to provide fast response on a mobile device by selectively generating Gabor features. The Gabor filter has been popularly used in face recognition to improve recognition performance. Since the computational effort for generating a Gabor feature is very large, it is important to use only the discriminative Gabor features on mobile devices which do not have sufficient computing power. At the same time, it is also important to maintain the recognition performance at an acceptable level. To reduce computational effort without degrading the recognition performance, the proposed method selectively generates Gabor features based on a contribution measure obtained by discriminant analysis. Face recognition is performed using only the selectively generated Gabor features, and the experimental results for the CMU Multi-PIE database and a real world data set show that the number of Gabor features can be reduced by more than 50% while keeping almost the same recognition performance. On a 624 MHz mobile phone, the execution time of feature extraction can be reduced to 19 ms from 46 ms on average.