Histogram Oriented Gradients-Gabor Hybrid Model for Feature Extraction for Enhanced Finger Vein Recognition Accuracy and Robustness
Keywords:
Finger vein, biometrics, feature extraction, algorithm, HOG, Gabor filterAbstract
Finger vein biometrics has gained significant attention due to its unique and reliable characteristics for personal identification. Feature extraction plays a crucial role in finger vein recognition systems by capturing discriminative patterns from finger vein images/maps. In this paper, we propose a hybrid feature extraction algorithm that combines the Histogram of Oriented Gradients (HOG) and Gabor filter techniques to enhance the representation of finger vein features. The HOG algorithm is known for its effectiveness in capturing local gradient information, while Gabor filters are capable of extracting fine texture details and orientation information. By integrating these two methods, we aim to leverage their complementary strengths and improve the discriminative power of the extracted features.
The proposed algorithm follows a multi-stage process. Firstly, the finger vein images/maps are preprocessed to enhance their quality and remove noise. Next, the HOG algorithm is applied to compute gradient orientations within local image patches, capturing the local texture and edge information. Simultaneously, Gabor filters are convolved with the preprocessed images to extract texture and orientation features at different scales and orientations. The resulting HOG and Gabor features are then concatenated to form a combined feature vector.
To evaluate the performance of the proposed hybrid algorithm, extensive experiments are conducted on a finger vein dataset consisting of a large number of samples. The extracted features are utilized for recognition tasks using various classification algorithms, such as Support Vector Machines (SVM) and Neural Networks (NN). The experimental results demonstrate that the hybrid HOG and Gabor filter algorithm outperforms individual feature extraction techniques, achieving higher accuracy and robustness in finger vein recognition. The proposed hybrid feature extraction algorithm provides a promising solution for effective finger vein biometrics. It harnesses the complementary nature of HOG and Gabor filters to capture both local gradient information and fine texture details, resulting in improved feature representation. The experimental results validate the effectiveness of the proposed approach and its potential for real-world applications in secure authentication systems, access control, and personal identification.
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