Real-time Gesture Recognition Using RFID Technology

This paper presents a real-time gesture recognition technique based on RFID technology. Inexpensive and unintrusive passive RFID tags can be easily attached to or interweaved into user clothes. The tag readings in an RFID-enabled environment can then be used to recognize the user gestures in order to enable intuitive human-computer interaction. People can interact with large public displays without the need to carry a dedicated device, which can improve interactive advertisement in public places. In this paper, multiple hypotheses tracking is used to track the motion patterns of passive RFID tags. Despite the reading uncertainties inherent in passive RFID technology, the experiments show that the presented online gesture recognition technique has an accuracy of up to 96%

The passive operating mode of the linear optical gesture sensor

The study evaluates the influence of natural light conditions on the effectiveness of the linear optical gesture sensor, working in the presence of ambient light only (passive mode). The orientations of the device in reference to the light source were modified in order to verify the sensitivity of the sensor. A criterion for the differentiation between two states: "possible gesture" and "no gesture" was proposed. Additionally, different light conditions and possible features were investigated, relevant for the decision of switching between the passive and active modes of the device. The criterion was evaluated based on the specificity and sensitivity analysis of the binary ambient light condition classifier. The elaborated classifier predicts ambient light conditions with the accuracy of 85.15%. Understanding the light conditions, the hand pose can be detected. The achieved accuracy of the hand poses classifier trained on the data obtained in the passive mode in favorable light conditions was 98.76%. It was also shown that the passive operating mode of the linear gesture sensor reduces the total energy consumption by 93.34%, resulting in 0.132 mA. It was concluded that optical linear sensor could be efficiently used in various lighting conditions.Comment: 10 pages, 14 figure

Mobiles and wearables: owner biometrics and authentication

We discuss the design and development of HCI models for authentication based on gait and gesture that can be supported by mobile and wearable equipment. The paper proposes to use such biometric behavioral traits for partially transparent and continuous authentication by means of behavioral patterns. © 2016 Copyright held by the owner/author(s)

Shape and Texture Combined Face Recognition for Detection of Forged ID Documents

This paper proposes a face recognition system that can be used to effectively match a face image scanned from an identity (ID) doc-ument against the face image stored in the biometric chip of such a document. The purpose of this specific face recognition algorithm is to aid the automatic detection of forged ID documents where the photography printed on the document’s surface has been altered or replaced. The proposed algorithm uses a novel combination of texture and shape features together with sub-space representation techniques. In addition, the robustness of the proposed algorithm when dealing with more general face recognition tasks has been proven with the Good, the Bad & the Ugly (GBU) dataset, one of the most challenging datasets containing frontal faces. The proposed algorithm has been complement-ed with a novel method that adopts two operating points to enhance the reliability of the algorithm’s final verification decision.Final Accepted Versio

Real-time Gesture Recognition Using RFID Technology

This paper presents a real-time gesture recognition technique based on RFID technology. Inexpensive and unintrusive passive RFID tags can be easily attached to or interweaved into user clothes. The tag readings in an RFID-enabled environment can then be used to recognize the user gestures in order to enable intuitive human-computer interaction. People can interact with large public displays without the need to carry a dedicated device, which can improve interactive advertisement in public places. In this paper, multiple hypotheses tracking is used to track the motion patterns of passive RFID tags. Despite the reading uncertainties inherent in passive RFID technology, the experiments show that the presented online gesture recognition technique has an accuracy of up to 96%