Proc. SPIE. 11430, MIPPR 2019: Pattern Recognition and Computer Vision
KEYWORDS: Code division multiplexing, Near infrared, Lithium, Facial recognition systems, Detection and tracking algorithms, Visualization, Databases, Feature extraction, Associative arrays, Simulation of CCA and DLA aggregates
Previous efforts on heterogeneous face recognition typically assume each subject has multiple training samples. However, this assumption may not hold in some special cases such as law-enforcement where only a Single Sample Per Person (SSPP) exists in the training set. For face recognition in SSPP scenario, it often suffers from overfitting and singular matrix problems. To solve this problem, we propose a novel learning-based algorithm called Coupled Discriminant Mapping (CDM) for heterogeneous face recognition. The CDM method finds a common space and learns a couple of discriminant projections for two different modalities without depending on the intra-class scatters. In the common space ,images of the same person are pulled into close proximity even if they come through different modalities meanwhile all the image under the same modality are pushed apart since each image belongs to a distinct class. The performance of CDM method is evaluated in two tasks: visual face image vs. near infrared face image and conventional face recognition. Experiments are conducted on two widely studied databases to show the effectiveness and consistence of the proposed CDM method.
In this paper, we investigate the degraded face recognition problem. At checkpoints, it is common that a passenger’s photo is digitally taken on the spot and compared with archived images scanned from printed photos. Therefore, the gallery set and the probe set come through two different media. The distortions introduced in the printing and the scanning processes often lead to unsatisfactory identification performance, necessitation further investigations in tackling degraded face recognition. Therefore, we propose an improved modality-invariant feature (IMIF) approach which combines the modality invariant features with a discriminative learning procedure to handle the variations in expression, occlusion and degradation. Experiments on the degraded face database show that the proposed IMIF enhances the degraded face recognition performance compared with other methods and validates the effectiveness of the proposed method.