As computers become more pervasive in society, facilitating natural human---computer interaction (HCI) will have a positive impact on their use. Hence, there has been growing interest in the development of new approaches and technologies for bridging the human---computer barrier. The ultimate aim is to bring HCI to a regime where interactions with computers will be as natural as an interaction between humans, and to this end, incorporating gestures in HCI is an important research area. Gestures have long been considered as an interaction technique that can potentially deliver more natural, creative and intuitive methods for communicating with our computers. This paper provides an analysis of comparative surveys done in this area. The use of hand gestures as a natural interface serves as a motivating force for research in gesture taxonomies, its representations and recognition techniques, software platforms and frameworks which is discussed briefly in this paper. It focuses on the three main phases of hand gesture recognition i.e. detection, tracking and recognition. Different application which employs hand gestures for efficient interaction has been discussed under core and advanced application domains. This paper also provides an analysis of existing literature related to gesture recognition systems for human computer interaction by categorizing it under different key parameters. It further discusses the advances that are needed to further improvise the present hand gesture recognition systems for future perspective that can be widely used for efficient human computer interaction. The main goal of this survey is to provide researchers in the field of gesture based HCI with a summary of progress achieved to date and to help identify areas where further research is needed.

/pdf/vision-based-hand-gesture-recognition-for-human-computer-3dczparvi2.pdf

Vision based hand gesture recognition for human computer interaction: a survey

A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Various recommender system techniques have been proposed since the mid-1990s, and many sorts of recommender system software have been developed recently for a variety of applications. Researchers and managers recognize that recommender systems offer great opportunities and challenges for business, government, education, and other domains, with more recent successful developments of recommender systems for real-world applications becoming apparent. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but more importantly of the practical developments in recommender systems. This paper therefore reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories: e-government, e-business, e-commerce/e-shopping, e-library, e-learning, e-tourism, e-resource services and e-group activities, and summarizes the related recommendation techniques used in each category. It systematically examines the reported recommender systems through four dimensions: recommendation methods (such as CF), recommender systems software (such as BizSeeker), real-world application domains (such as e-business) and application platforms (such as mobile-based platforms). Some significant new topics are identified and listed as new directions. By providing a state-of-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in recommender system applications. Research papers on various recommender system applications are summarized.The recommender systems are examined systematically through four dimensions.The recommender system applications are classified into eight categories.Related recommendation techniques in each category are identified.Several new recommendation techniques and application areas are uncovered.

Recommender system application developments

Emotion recognition in human-computer interaction

Species knowledge is essential for protecting biodiversity. The identification of plants by conventional keys is complex, time consuming, and due to the use of specific botanical terms frustrating for non-experts. This creates a hard to overcome hurdle for novices interested in acquiring species knowledge. Today, there is an increasing interest in automating the process of species identification.
 The availability and ubiquity of relevant technologies, such as, digital cameras and mobile devices, the remote access to databases, new techniques in image processing and pattern recognition let the idea of automated species identification become reality. This paper is the first systematic literature review with the aim of a thorough analysis and comparison of primary studies on computer vision approaches for plant species identification. We identified 120 peer-reviewed studies, selected through a multi-stage process, published in the last 10 years (2005–2015). After a careful analysis of these studies, we describe the applied methods categorized according to the studied plant organ, and the studied features, i.e., shape, texture, color, margin, and vein structure. Furthermore, we compare methods based on classification accuracy achieved on publicly available datasets. Our results are relevant to researches in ecology as well as computer vision for their ongoing research. The systematic and concise overview will also be helpful for beginners in those research fields, as they can use the comparable analyses of applied methods as a guide in this complex activity.

/pdf/plant-species-identification-using-computer-vision-50ff22bjdd.pdf

Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review

This article describes research and development on car noise and vibration by the German car manufacturer Opel. Opel now has one of the world's largest anechoic chambers, 22m long by 16m wide. It can measure 'pass-by' noise in any weather, and has a powertrain test rig, which allows the whole powertrain and exhaust system to be examined away from the bodywork. By making vibrations visible, holography is helping to reduce the development time of a new car model by up to two months; it uses the interference principle to analyse structures without actual contact. Using holographic patterns, acoustic experts can work with design engineers on modifications to eliminate vibration. Psychoacoustics is perhaps the most exciting area of research. It applies the theory that sound can counter behaviour as well as influence it. If a driver is being aggressive or driving too fast, sound inside the car can be used to alter his driving style. By 'learning' a driver's personality, a car could take appropriate action to ensure safety and comfort by producing pleasant sounds in the car. However, these methods are still in the experimental stage. Opel is also conducting research on the stressful effects of noise and vibrations in cars.

The science of sound

In this paper, we propose an enhanced multimodal personal authentication system for mobile device security. The proposed approach fuses information obtained from face, teeth and voice modalities to improve performance. To integrate three modalities, we employ various fusion techniques such as the weighted-summation rule, K-NN, Fisher and Gaussian classifiers, and we then evaluate the authentication performance of the proposed system. The performance is evaluated on a database consisting of 1000 biometric traits that correspond to the face, teeth and voice modalities of 50 persons, i.e., 20 biometric traits per individual, in which these biometric traits are simultaneously collected by a smart-phone device. The experiment results integrating the three modalities showed the error rates of 1.64%, 4.70%, 3.06% and 1.98% for the weighted-summation rule, K-NN, Fisher and Gaussian classifier, respectively, and that the weight-summation rule outperformed the other classification approaches. In contrast, the error rates regarding a single modality were 5.09%, 7.75% and 8.98% for face, teeth, and voice modalities, respectively. From these results, we confirmed that the proposed method achieved a significant performance improvement over the methods using a single modality, and the results showed that the proposed method was very effective through various fusion experiments.

Person authentication using face, teeth and voice modalities for mobile device security

Mobile devices such as smart-phone, PDA and mobile-phone are vulnerable to theft and loss due to their small size and the characteristics of the environments in which they are used. A simple and convenient authentication system is required to protect private information stored in the mobile device. Therefore, we propose a new multimodal biometric authentication approach using teeth image and voice as biometric traits in this paper. The individual matching scores obtained from the teeth image and voice are combined using a weighted-summation operation, and the fused-score is utilized to classify an unknown user into the acceptance or rejection. The proposed method is evaluated using 1000 teeth images and voices, in which these are collected by smart-phone, i.e., one mobile device for 50 subjects. In the experiment results, the proposed method has an EER of 2.13%, and we demonstrate the effectiveness of the proposed method.

Multimodal biometric authentication using teeth image and voice in mobile environment

In this paper, we propose and implement a leaf recognition system using the leaf vein and shape that can be used for plant classification. The proposed approach uses major main vein and frequency domain data by using Fast Fourier Transform (hereinafter, FFT) methods with distance between contour and centroid on the detected leaf image. Total 21 leaf features were extracted for the leaf recognition, which they include ① the distance feature between centroid and all points on the leaf contour, ② frequency domain data by FFT that was performed using the distances. In summary, 10 features of all the 21 leaf features were extracted using distance, FFT magnitude, and phase, the other 10 features were extracted using t the digital morphological features using four basic geometric features and five vein features, and the last 1 feature was extracted using the convex hull. To verify the validity of the approach, images of 1907 leaves apply to classify 32 kinds of plants. In the experimental results, the proposed leaf recognition system showed an average recognition rate of 97.19%, and we can confirm that the recognition rate of the proposed leaf recognition system was better than that of the existed leaf recognition method.

An Implementation of Leaf Recognition System using Leaf Vein and Shape

In this paper, we propose a real-time hand gesture recognition system based on difference image entropy using a stereo camera. Existing systems use hand detection primarily with some type of marker. Our system, however, uses a real-time hand image recognition system. In the detection step, we implement a depth map using a sum of absolute differences based on the acquired right-left image using a stereo camera. This system detects a foreground object and perceives it as a hand. The hand gesture recognition system uses the difference image entropy of the input image and the average image. To evaluate the performance of the proposed technology, we implement a recognition experiment using the hand gesture 240 database. The proposed method shows an average recognition rate of 85%. Using the proposed method, we implement a Chinese chess game based on hand gesture recognition.

Game interface using hand gesture recognition

The viewing set-based method has difficulties ensuring that a user will enjoy recommended programs, and the model-based collaborative filtering method contains system-side real-time recommendation problems because most recent ratings cannot be applied in the recommendations and it has increased calculating costs due to the training process. In this paper, we propose a personalized program recommender for smart TVs using memory-based collaborative filtering with a novel similarity method that is robust to cold-start conditions and faster than the often-used, existing similarity method. The proposed method can improve the recommendation performance of electronic program guides and recommender applications for smart TVs. We determined the prediction accuracy of the ratings under various conditions in order to evaluate the proposed method. As a result, we confirmed that the proposed method is effective for cold-start conditions.

Kwang-Seok Hong

Papers

Person authentication using face, teeth and voice modalities for mobile device security

Multimodal biometric authentication using teeth image and voice in mobile environment

An Implementation of Leaf Recognition System using Leaf Vein and Shape

Game interface using hand gesture recognition

Personalized smart TV program recommender based on collaborative filtering and a novel similarity method