Face verification/recognition is a tough challenge in comparison to
identification based on other biometrics such as iris, or fingerprints. Yet, due to its unobtrusive nature, the face is naturally suitable for security related applications. Face verification process relies on feature extraction from face images. Current schemes are either geometric-based or template-based. In the latter, the face image is statistically analysed to obtain a set of feature vectors that best describe it. Performance of a face verification system is affected by image variations due to illumination, pose, occlusion, expressions and scale. This paper extends our recent work on face verification for constrained platforms, where the feature vector of a face image is the coefficients in the wavelet transformed LL-subbands at depth 3 or more. It was demonstrated that the wavelet-only feature vector scheme has a comparable performance to sophisticated state-of-the-art when
tested on two benchmark databases (ORL, and BANCA). The significance of those results stem from the fact that the size of the k-th LL- subband is 1/4k of the original image size. Here, we investigate the use of wavelet coefficients in various subbands at level 3 or 4 using various wavelet filters. We shall compare the performance of the wavelet-based scheme for different filters at different subbands with a number of state-of-the-art face verification/recognition schemes on two benchmark databases, namely ORL and the control section of BANCA. We shall demonstrate that our schemes have comparable performance to (or outperform) the best performing other schemes.