Fast and efficent multimodal eye biometrics using projective dictionary pair learning

Abstract

This work proposes a projective pairwise dictionary learning-based approach for fast and efficient multimodal eye biometrics. The work uses a faster Projective pairwise Discriminative Dictionary Learning (DL) in contrast to the traditional DL which uses synthesis DL. Projective Pairwise Discriminative Dictionary (PPDD) uses a synthesis dictionary and an analysis dictionary jointly to achieve the goal of pattern representation and discrimination. As the PPDD process of DL is in contrast to the use of l0 or l1-norm sparsity constraints on the representation coefficients adopted in most traditional DL, it works faster than other DL. Moreover, the blending of synthesis dictionary and an analysis dictionary also enhance the feature representation of the complex eye patterns. We employed the combination of sclera and iris traits to establish multimodal biometrics. The experimental study and analysis conducted fulfill the hypothesis we considered. In this work we employed a part of the UBIRIS version 1 dataset to conduct the experiments.

SSERBC 2017: Sclera segmentation and eye recognition benchmarking competition

“…The systems have not achieved higher performance with respect to the other algorithms proposed in SSRBC 1 and the work of [7] have achieved better performance. Perhaps cutting age featuring [12,13] and classification method are required investigating this subject of research to attend better recognition performance.…”

Subject-Independent EEG Emotion Recognition Based on Genetically Optimized Projection Dictionary Pair Learning

“…The discriminative model in Equation (1) aims to train a synthesis dictionary D that can sparsely represent the signal F [24,27]. Unfortunately, obtaining the code A for this dictionary requires an expensive l rnomm sparse coding process.…”

“…The Projection Dictionary Pair Learning (PDPL) algorithm is a widely used machine learning technique employed in diverse applications, such as image processing [24], natural language processing [25], and Internet of Medical Things (IoMT) systems [26]. The PDPL serves as an effective approach for extracting significant features from high-dimensional data [27] and extends the standard Projection Dictionary Learning (PDL) algorithm.…”