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Usually, most of the data generated in real-world such as images, speech signals, or fMRI scans has a high dimensionality. Therefore, dimensionality reduction techniques can be used to reduce the number of variables in that data and then the system performance can be improved. Because the processing of the high dimensional data leads the increase of complexity both in execution time and memory usage...
This paper has proposed gait recognition approach for analyzing and classifying human identification under carrying a bag and wearing a clothing thus improving recognition performances. The proposed method is based on detail wavelet features extracted from the Haar-wavelet decomposition of dynamic areas in the Gait Energy Image (GEI). Spectral Regression Kernel Discriminant Analysis (SRKDA) is then...
Real face recognition is a challenging problem especially when face images are subject to distortions. This paper presents an approach to tackle partial occlusion distortions present in real face recognition using a single training sample per person. First, original images are partitioned into multiple blocks and Local Binary Patterns are applied as a local descriptor on each block separately. Then,...
Handwriting has been known to be a very strong identifying characteristic of an individual and can be considered a behavioural biometric trait. This has made hand writer identification an important area of research. In this paper, a novel offline writer identification system is proposed using ensemble of multi-scale local ternary pattern histogram features. Features are extracted at multiple scales...
Fusing multiple features within one biometric modality has attracted increasing attention and interest among researchers during recent decades because the concept is useful in addressing a wide range of real world problems. In this paper, we propose a novel fusion approach that combines two feature extraction algorithms: Local Binary Pattern Histogram Fourier Features (LBP-HF) and Gabor filter technique...
With the rapid development of Internet-of-Things (IoT), face scrambling has been proposed for privacy protection during IoT-targeted image/video distribution. Consequently, in these IoT applications, biometric verification needs to be carried out in the scrambled domain, presenting significant challenges in face recognition. Since face models become chaotic signals after scrambling/encryption, a typical...
This paper proposes a supervised feature extraction approach which is capable to select distinctive features for the recognition of human gait under clothing and carrying conditions thus improving the recognition performances. The principle of the suggested approach is based on the use of feature texture descriptors extracted from Gait Energy Image (GEI). The proposed features are computed using the...
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