There has been many attempts to make authentication processes more robust. Biometric techniques are one among them. Biometrics is unique to an individual and hence their usage can overcome most of the issues in conventional authentication process. This paper makes a scrutinizing study of the existing biometric techniques, their usage and limitations pertaining to their deployment in real time cases. It also deals with the motivation behind adapting biometrics in present day scenarios. The paper also makes an attempt to throw light on the technical and security related issues pertaining to biometric systems.

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Face recognition system is one of the robust means of authentication. It involves comparing the faces of an individual against a set of images in the training database. Thus the security issues pertaining to the training database is very critical. This paper aims at providing security to the images in the training database by empowering the encryption algorithms using a secure Random Number Generator (RNG). To facilitate this, the seismic waves are used as seeds to drive the Pseudo-Random Number Generators (PRNGs). The efficiency of seismic waves as a True Random Number Generator (TRNG) was evaluated using two statistical suites. Also, the proposed TRNG is compared against other existing RNGs. It was found that the degree of randomness rendered by the proposed system was in good agreement like the other existing generators. The proposed system was found to be cost-effective, portable and easy to maintain.

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.

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