12 research outputs found

    Unsupervised Segmentation of Action Segments in Egocentric Videos using Gaze

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    Unsupervised segmentation of action segments in egocentric videos is a desirable feature in tasks such as activity recognition and content-based video retrieval. Reducing the search space into a finite set of action segments facilitates a faster and less noisy matching. However, there exist a substantial gap in machine understanding of natural temporal cuts during a continuous human activity. This work reports on a novel gaze-based approach for segmenting action segments in videos captured using an egocentric camera. Gaze is used to locate the region-of-interest inside a frame. By tracking two simple motion-based parameters inside successive regions-of-interest, we discover a finite set of temporal cuts. We present several results using combinations (of the two parameters) on a dataset, i.e., BRISGAZE-ACTIONS. The dataset contains egocentric videos depicting several daily-living activities. The quality of the temporal cuts is further improved by implementing two entropy measures.Comment: To appear in 2017 IEEE International Conference On Signal and Image Processing Application

    3D face recognition using kernel-based PCA approach

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    Face recognition is commonly used for biometric security purposes in video surveillance and user authentications. The nature of face exhibits non-linear shapes due to appearance deformations, and face variations presented by facial expressions. Recognizing faces reliably across changes in facial expression has proved to be a more difficult problem leading to low recognition rates in many face recognition experiments. This is mainly due to the tens degree-of-freedom in a non-linear space. Recently, non-linear PCA has been revived as it posed a significant advantage for data representation in high dimensionality space. In this paper, we experimented the use of non-linear kernel approach in 3D face recognition and the results of the recognition rates have shown that the kernel method outperformed the standard PCA. © Springer Nature Singapore Pte Ltd. 2019
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