A real-time head pose and gaze estimation (HPGE) algorithm has excellent potential for technological advancements either in human–machine or human–robot interactions. For example, in high-accuracy advent applications such as Driver’s Assistance System (DAS), HPGE plays a crucial role in omitting accidents and road hazards. In this paper, the authors propose a new hybrid framework for improved estimation by combining both the appearance and geometric-based conventional methods to extract local and global features. Therefore, the Zernike moments algorithm has been prominent in extracting rotation, scale, and illumination invariant features. Later, conventional discriminant algorithms were used to classify the head poses and gaze direction. Furthermore, the experiments were performed on standard datasets and real-time images to analyze the accuracy of the proposed algorithm. As a result, the proposed framework has immediately estimated the range of direction changes under different illumination conditions. We obtained an accuracy of ~85%; the average response time was 21.52 and 7.483 ms for estimating head poses and gaze, respectively, independent of illumination, background, and occlusion. The proposed method is promising for future developments of a robust system that is invariant even to blurring conditions and thus reaching much more significant performance enhancement.

The extraction of required features from the facial image is an important primitive task for face recognition. This paper evaluates different nonlinear feature extraction approaches, namely wavelet transform, radon transform and cellular neural networks (CNN). The scalability of the linear subspace techniques is limited as the computational load and memory requirements increase dramatically with the large database. In this work, the combination of radon and wavelet transform based approach is used to extract the multi-resolution features, which are invariant to facial expression and illumination conditions. The efficiency of the stated wavelet and radon based nonlinear approaches over the databases is demonstrated with the simulation results performed over the FERET database. This paper also presents the use of CNN in extracting the nonlinear facial features in improving the recognition rate as well as computational speed compared to other stated nonlinear approaches over the ORL database.