Illumination Invariant Face Recognition Using Quaternion-Based Correlation Filters
Existing face recognition systems decrease their performance when face images are affected by lighting variations. Recently, several quaternionic representations of face image features and a quaternion-based correlation filter have been combined in order to cope with the effects of having non-properly illuminated face images. The use of this approach has the advantage of using only one training face image per person. In this paper, the original idea based on the unconstrained optimal trade-off quaternion filter (UOTQF) is extended and two additional different correlation filters in quaternionic domain are evaluate: a phase only quaternion filter (POQF) and a separable trade-off quaternion filter (STOQF). Three different quaternion-based correlation filters are designed and conjugated with four face feature extraction methods aiming at obtaining the best combination: a two-level discrete wavelet decomposition (DWT), image differentiation (DIF), discrete cosine transform (DCT) and local binary patterns (LBP). Verification and identification experiments confirms that when combining a quaternionic representation with a quaternion-based correlation filter, both with good discriminative power and illumination invariant properties, an improvement in face recognition accuracy is obtained.
illumination invariant face recognition quaternions correlation filters