2011 international joint conference on neural networks (ijcnn)

Despite the importance of sign language recognition systems, there is a lack of a Systematic Literature Review and a classification scheme for it. This is the first identifiable academic literature review of sign language recognition systems. It provides an academic database of literature between the duration of 2007–2017 and proposes a classification scheme to classify the research articles. Three hundred and ninety six research articles were identified and reviewed for their direct relevance to sign language recognition systems. One hundred and seventeen research articles were subsequently selected, reviewed and classified. Each of 117 selected papers was categorized on the basis of twenty five sign languages and were further compared on the basis of six dimensions (data acquisition techniques, static/dynamic signs, signing mode, single/double handed signs, classification technique and recognition rate). The Systematic Literature Review and classification process was verified independently. Literature findings of this paper indicate that the major research on sign language recognition has been performed on static, isolated and single handed signs using camera. Overall, it will be hoped that the study may provide readers and researchers a roadmap to guide future research and facilitate knowledge accumulation and creation into the field of sign language recognition.

Sign Language Recognition Systems: A Decade Systematic Literature Review

Accurate diagnosis of Autism Spectrum Disorder (ASD) is essential for its management and rehabilitation Neuroimaging techniques that are non-invasive are disease markers and may be leveraged to aid ASD diagnosis Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain Due to the intricate structure and function of the brain, diagnosing ASD with neuroimaging data without exploiting artificial intelligence (AI) techniques is extremely challenging AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally In this paper, studies conducted with the aid of DL networks to distinguish ASD were investigated Rehabilitation tools provided by supporting ASD patients utilizing DL networks were also assessed Finally, we presented important challenges in this automated detection and rehabilitation of ASD

Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review

The aim of an automatic video-based facial expression recognition system is to detect and classify human facial expressions from image sequence. An integrated automatic system often involves two components: 1) peak expression frame detection and 2) expression feature extraction. In comparison with the image-based expression recognition system, the video-based recognition system often performs online detection, which prefers low-dimensional feature representation for cost-effectiveness. Moreover, effective feature extraction is needed for classification. Many recent recognition systems often incorporate rich additional subjective information and thus become less efficient for real-time application. In our facial expression recognition system, first, we propose the double local binary pattern (DLBP) to detect the peak expression frame from the video. The proposed DLBP method has a much lower-dimensional size and can successfully reduce detection time. Besides, to handle the illumination variations in LBP, logarithm-laplace (LL) domain is further proposed to get a more robust facial feature for detection. Finally, the Taylor expansion theorem is employed in our system for the first time to extract facial expression feature. We propose the Taylor feature pattern (TFP) based on the LBP and Taylor expansion to obtain an effective facial feature from the Taylor feature map. Experimental results on the JAFFE and Cohn-Kanade data sets show that the proposed TFP method outperforms some state-of-the-art LBP-based feature extraction methods for facial expression feature extraction and can be suited for real-time applications.

Facial Expression Recognition From Image Sequence Based on LBP and Taylor Expansion

The application of EEG power for the prediction and interpretation of consumer decision-making: A neuromarketing study

Evolutionary perspective for optimal selection of EEG electrodes and features

Currently, gesture recognition from continuous video sequences is one of the most exciting research areas. This paper proposes a novel HMM-based gesture recognition scheme that can be implemented for developing an improved HCI system capable of providing enhanced performance. This framework explores the high potential of Microsoft's Kinect sensor in gesture recognition by utilizing it in the data acquisition phase. The primary novelty of the work lies in the choice of an active difference signature-based feature descriptor that contains time-warped information in a single sequence over the classically used geometric features. The discussed framework has been tested for 12 distinct gestures embodied by 60 different subjects and it is important to note that for all the gestures the proposed scheme has attained a fairly high recognition rate of nearly 90% which proves the worth of the present work in real time applications. Further, to check the efficacy of the newly formulated framework the performance of the same has been validated against the existing standard technologies.

HMM-based gesture recognition system using kinect sensor for improvised human-computer interaction

In this paper, we describe components that form a part of USCSAIL team’s submissions to Track 1 and Track 2 of the second DIHARD speaker diarization challenge. We describe each module in our speaker diarization pipeline and explain the rationale behind our choice of algorithms for each module, while comparing the Diarization Error Rate (DER) against different module combinations. We propose a clustering scheme based on spectral clustering that yields competitive performance. Moreover, we introduce an overlap detection scheme and a re-segmentation system for speaker diarization and investigate their performances using controlled and in-the-wild conditions. In addition, we describe the additional components that will be integrated to our speaker diarization system. To pursue the best performance, we compare our system with the stateof-the-art methods that are presented in the previous challenge and literature. We include preliminary results of our speaker diarization system on the evaluation data from the second DIHARD challenge.

The Second DIHARD Challenge: System Description for USC-SAIL Team.

With the growing interest in the domain of human computer interaction (HCI) these days, budding research professionals are coming up with novel ideas of developing more versatile and flexible modes of communication between a man and a machine. Using the attributes of internet, the scientists have been able to create a web based social platform for learning any desired art by the subject himself/herself, and this particular procedure is termed as electronic learning or e-learning. In this paper, we propose a novel application of gesture dependent e-learning of dance. This e-learning procedure may provide help to many dance enthusiasts who cannot learn the art because of the scarcity of resources despite having great zeal. The paper mainly deals with recognition of different dance gestures of a trained user such that after detecting the discrepancies between the gestures shown and actually performed by a novice; the user can rectify his faults. The elementary knowledge of geometry has been employed to introduce the concept of planes in the feature extraction stage. Actually, five planes have been constructed to signify major body parts while keeping the synchronous parts in one unit. Then four distances and four angular features have been obtained to provide entire positional information of the different body joints. Finally, using a probabilistic neural network the dance gestures have been classified after training the said network with sufficient amount of data recorded from numerous subjects to maintain generality.

Human skeleton matching for e-learning of dance using a probabilistic neural network

Color perception is one of the most important cognitive features of human brain. Different colors lead to different cognitive activities and different mental arousal levels; as revealed by power spectral density obtained from EEG signals. As the color plays an important role in marketing and packaging industry; so neuro-marketing research; based on color stimuli is considered to be an important tool for market researcher. In order to assess the impact of each color; prime focus is to detect different colors from EEG signals. This study employs four color stimuli; e.g. red; green; yellow and blue; that were shown to various subjects and EEG signal corresponding to the mentioned stimulus was acquired. Power spectral density of each color was estimated and different activation areas of brain for each stimulus is illustrated in figures. This paper also employs an Interval-Type-II fuzzy space classifier for distinguishing between different stimuli that are considered for the concerned experiment. It is noted that classification rate is maximum for red color and minimum in case of yellow color. Precision and recall values also have been highlighted here. For detailed analysis; the performance of IT2FS classifier has been compared with other standard classifiers by Friedman Test.

Discriminating different color from EEG signals using Interval-Type 2 fuzzy space classifier (a neuro-marketing study on the effect of color to cognitive state)

Rimita Lahiri

Papers

Evolutionary perspective for optimal selection of EEG electrodes and features

HMM-based gesture recognition system using kinect sensor for improvised human-computer interaction

The Second DIHARD Challenge: System Description for USC-SAIL Team.

Human skeleton matching for e-learning of dance using a probabilistic neural network

Discriminating different color from EEG signals using Interval-Type 2 fuzzy space classifier (a neuro-marketing study on the effect of color to cognitive state)