Each individual describes unique patterns during their gait cycles. This information can be extracted from the live video stream and used for subject identification. In appearance based recognition methods, this is done by tracking silhouettes of persons across gait cycles. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. When such sensors are used for gait recognition, existing RGB appearance based methods can be extended to get a substantial gain in recognition accuracy. In this paper, this is accomplished using information fusion techniques that combine features from extracted silhouettes, used in traditional appearance based methods, and the height feature that can now be estimated using depth data. The latter is estimated during the silhouette extraction step with minimal additional computational cost. Two approaches are proposed that can be implemented easily as an extension to existing appearance based methods. An extensive experimental evaluation was performed to provide insights into how much the recognition accuracy can be improved. The results are presented and discussed considering different types of subjects and populations of different height distributions.

This paper looks at and describes the potential of using artificial intelligence in smart environments. Various environments such as houses and residential and commercial buildings are becoming smarter through the use of various technologies, i.e., various sensors, smart devices and elements based on artificial intelligence. These technologies are used, for example, to achieve different levels of security in environments, for personalized comfort and control and for ambient assisted living. We investigated the deep learning approach, and, in this paper, describe its use in this context. Accordingly, we developed four deep learning models, which we describe. These are models for hand gesture recognition, emotion recognition, face recognition and gait recognition. These models are intended for use in smart environments for various tasks. In order to present the possible applications of the models, in this paper, a house is used as an example of a smart environment. The models were developed using the TensorFlow platform together with Keras. Four different datasets were used to train and validate the models. The results are promising and are presented in this paper.

This paper proposes and presents one approach for people identification based on image and additional physical parameters, height and step length, of a person comparison. People identification is very important in many areas of human life. There are large number of identification methods (biometric methods) that include a different scope of methods, for example fingerprint identification, hand geometry identification, facial recognition, methods based on human eye identification (retina and iris), gait recognition etc. Most of that methods require some kind of interaction with a person, what could be a problem in many practical applications. The method that does not require any interaction with a person is gait recognition. One approach for a people identification based on gait recognition, that uses silhouettes of a person and parameters of person height and step length, is proposed and presented in this paper.

This paper considers, proposes, and describes ways and possibilities to implement various biometric methods based on deep learning in different types of smart environments. The main objective is to investigate and propose how these methods can be applied in smart environments such as office buildings, collective residential buildings, and houses, primarily to enhance security. To this end, three biometric methods are analyzed and proposed for use: gait, face, and palmprint recognition. Accordingly, this paper describes possible applications of gait, face, and palmprint recognition for person identification in smart environments, specifically in an office building and a house. Both gait and face recognition can be used without interaction with the person being identified. Palmprint recognition, however, requires interaction, as the person must place their palm on a designated device. These three methods were developed using deep learning models. The TensorFlow platform and Keras are used for model development. The architecture of the models and related settings are described. The training and validation processes were conducted using four datasets that were publicly available or available on request: a gait dataset, a face dataset, and two palmprint datasets. The results primarily related to validation accuracy are presented in the paper. In addition to validation accuracy, results for precision, recall, and F1-score are also reported. Furthermore, the reasons for choosing these three methods, along with their advantages and disadvantages, are discussed.