Volume V | Issue 2 | 2026 | Paper ID: IJATEM-V05I02P3

HDG and HDGG: An External Feature for Facial Expression Using Machine Learning

Author(s)

K. Muralikrishna, S. Chandana, Sk. Saniya Siddikha, K. Santhi Priya, S. Raghavamma

Cite This

Muralikrishna. K, S. Chandana, Sk. Saniya Siddikha, K. Santhi Priya, S. Raghavamma, "HDG and HDGG: An External Feature for Facial Expression Using Machine Learning", International Journal of Advanced Trends in Engineering and Management, vol. 05, no. 02, pp. 25-31, 2026.

Abstract

Facial Emotion Recognition (FER) is a significant area of research in affective computing, which focuses on automatically identifying human emotions from facial images. Although deep learning methods have demonstrated promising results in terms of accuracy, their need for large amounts of data and computational resources makes them less suitable for real-time applications. In this paper, a machine learning-based approach for FER is proposed using Histogram of Directional Gradient (HDG) and Histogram of Directional Gradient Generalized (HDGG) features. The proposed features are designed to capture directional gradient information, which assists in achieving compact and robust feature sets. The features are then classified using supervised machine learning classifiers to detect basic emotional categories. The experimental results on standard facial expression databases demonstrate that the proposed HDG and HDGG features are comparable in recognition accuracy while retaining low-dimensional and computationally efficient features. The results demonstrate that the proposed approach provides a feasible solution for real-time facial emotion recognition systems.

Keywords

Facial Emotion Recognition (FER), HDG and HDGG Features, Machine Learning, Facial Expression Recognition, Supervised Classification

References

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