A Hyperspectral camera provides discriminating features for capturing human faces that cannot be obtained by any other imaging technique. Nevertheless, it has new issues comprising curse of dimensionality, physical parameter retrieval, fast computing and inter band misalignment. As a result of, the literature of Hyperspectral Face Recognition is more scanty and confined to improvised dimensionality reduction and minimization of wide-ranging bands, and thus the objective can be obtained by the use of the Convolution Neural Network (ConvNet). Since ConvNet is of great attention in recent times, it can offer outstanding performance in face recognition systems, where the quantity of training data is amply large. We propose a Hyperspectral Face Recognition system using Firefly algorithm for band fusion and the Convolution Neural Network for classification. In addition to this, the present work is extended 11 exiting face recognition methods to perform Hyperspectral Face Recognition task. Thus the work has been framed as Hyperspectral Face Recognition problem to an image-set classification problem and assessment of the performance has been done on six state-of-the-art image-set problem techniques, and similarly it was examined on five state-of-the-art RGB and gray scale face recognition system, subsequently applied improved Firefly band selection algorithm on Hyperspectral Images to get appropriate band. Assessment with the eleven extended and five existing HSI Face Recognition system on two benchmark datasets (CMU-HSFD & UWA-HSFD) demonstrates that the proposed system overtakes all by a noteworthy margin. Lastly, we execute the band selection demonstration to get the novelty for most informative bands in Visible Near Infrared (VNIR).

The need for personalized surveillance systems for elderly health care has risen drastically. However, recent methods involving the usage of wearable devices for activity monitoring offer limited solutions. To address this issue, we have proposed a system that incorporates a vision-based deep learning solution for elderly surveillance. This system primarily consists of a novel multi-feature-based person tracker (MFPT), supported by an efficient vision-based person fall detector (VPFD). The MFPT encompasses a combination of appearance and motion similarity in order to perform effective target association for object tracking. The similarity computations are carried out through Siamese convolutional neural networks (CNNs) and long-short term memory (LSTM). The VPFD employs histogram-of-oriented-gradients (HoGs) for feature extraction, followed by the LSTM network for fall classification. The cloud-based storage and retrieval of objects is employed allowing the two models to work in a distributed manner. The proposed system meets the objectives of ITU Focus Group on AI for Health (FG-AI4H)under the category, “falls among the elderly”. The system also complies with ITU-T F.743.1 standard, and it has been evaluated over benchmarked object tracking and fall detection datasets. The evaluation results show that our system achieves the tracking precision of 94.67% and the accuracy of 98.01% in fall detection, making it practical for health care system use. The HoG feature-based LSTM model is a promising item to be standardized in ITU for fall detection in elderly healthcare management under the requirements and service description provided by ITU-T F.743.1.