Beyond Pixels: Transforming Face Recognition with Curvelet and Bidirectional Neighborhood Preservation

Enhancing Face Recognition with Curvelet and Bidirectional Neighborhood Preservation

Abstract

In recent years, face recognition has gained significant attention as a crucial technology invarious applications, including security, surveillance, and human-computer interaction. This abstractintroduces a novel approach to face recognition by combining the Curvelet Transform and BidirectionalTwo-Dimensional Neighborhood Preservation Projection (BD2D-NPP) methods. This innovative fusion oftechniques offers a powerful solution to address the challenges of face recognition in varying lightingconditions, occlusions, and pose variations.The Curvelet Transform, known for its multiresolution and directional analysis capabilities, is employedto extract relevant features from face images. By preserving key information about the facial contoursand textures at multiple scales, it enhances the robustness of the recognition system. Additionally,BD2D-NPP, a bidirectional projection technique, is employed to reduce the dimensionality of the featurevectors while preserving the essential neighborhood relationships among data points. This ensures thatimportant facial characteristics are retained during dimensionality reduction.Our proposed approach combines the Curvelet Transform and BD2D-NPP for feature extraction anddimensionality reduction, respectively, resulting in a powerful face recognition system. The fusion ofthese methods enables accurate recognition of faces even in challenging scenarios, such as low-lightconditions or partial face occlusions. Experiments on benchmark face recognition datasets demonstratethe effectiveness of the proposed approach in achieving high recognition accuracy and robustness.This research contributes to the advancement of face recognition technology, offering a promisingsolution for real-world applications where accurate and reliable face identification is of paramountimportance. The combined power of the Curvelet Transform and BD2D-NPP brings us closer todeveloping more efficient and reliable face recognition systems that can be deployed in a wide range ofpractical scenarios.