A Novel Gradient and Statistical Feature-Based Local Pattern Descriptor for Enhanced Face Recognition
In the field of computer vision, face recognition is a critical research area that has many applications in different fields such as security and medical treatment to authentication systems. Tradition feature descriptors are popular, but they are often handicapped by problems such as changes in lighting, posture and facial expression. While these techniques encode certain features well, they are subject to a number of biases including light sensitivity and computational complexity. In this paper, we present a new feature descriptor, the Directional Intensity Pattern (DIP) descriptor. It is an excellent combination of local texture, gradient magnitude and direction features. Feature selection and dimensionality reduction: Principal Component Analysis (PCA) for dimension reduction to improve discriminative power and less redundancy The Least Absolute Shrinkage and Selection Operator (LASSO) is used for feature selection. Furthermore, pre-processing techniques such as gamma correction and contrast normalization improved lightness invariance, thus increasing recognition performance. In this work, the DIP descriptor was evaluated on two public available datasets (YaleB, Face96). The results showed that it could achieve 97.59% and 98.36% accuracy on these datasets respectively, higher than the state-of-the-art methods. The result confirmed DIP descriptor remarkable ability to grasp quite a few texture and structure features of the picture in this manner it provides a powerful framework for face recognition under various circumstances.
Volume & Issue
Vol. Volume 20 / Iss. Issue 2