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.

Stress emotion recognition from speech utterances seeks a humongous attention among the researchers. Stress is one of the serious problems in the current society. Due to this people in different sectors are suffering from many severe health issues which may lead to huge economic damage. In this paper a detailed survey has been made on the recognition of stress emotion of speech samples which addresses two important areas. The primary one deals with the suitable databases which are used for stress emotion recognition. The later one deals with the choice of best features and also the classifiers along with their performances used for stress emotion recognition. Conclusion deals with the performance evaluations and limitations of the stress emotion recognition system.