Assessment of the effect of variations in Eye Blinks on a face recognition algorithm

Currently face recognition has reached a certain degree of maturity when operating under constrained environments. When it comes to real time situations, the system degrades sharply in handling variations like illumination, occlusions, skin tone, cosmetics, image misalignment, age, pose, etc., inherent in the face images acquired. Hence understanding and eliminating the effects of each of these factors is crucial to any face recognition system. This paper deals with studying the effect of variances in the Eye Blink Strengths (EBS) on a face image undergoing face recognition, thereby testing the efficiency of face recognition algorithm. The study makes exclusive usage of Brain Computer Interface (BCI) technology to detect eye blinks and to measure their corresponding EBS values using Electroencephalograph (EEG) device. The face recognition algorithm under test was the amalgamation of Principal Component Analysis (PCA), Local Binary Pattern (LBP) based feature extraction and Support Vector Machine (SVM) based classification. EBS is assessed using an inexpensive, portable, non-invasive EEG device. The efficiency of the face recognition algorithm to withstand the eye blinks with varying degree of EBS values for the given face images was determined. It was found that the proposed methodology of test case generation can be effectively be used to evaluate various other face recognition algorithms against varying eye blinks.