Multi-view face recognition from single RGBD models of the faces

Kim, Dong-Hun; Comandur, Bharath; Medeiros, Henry; Elfiky, Noha M.; Kak, Avinash C.

doi:10.1016/j.cviu.2017.04.008

Cited by 8 publications

(3 citation statements)

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“…Eq. (8) used to outputs any given RGB color vector of the values a, b, and c, respectively [16]. For the preprocessing process a normalization is an indispensable way that makes a descriptor independent from lighting changes.…”

Section: Resultsmentioning

confidence: 99%

“…The study outputs were pretty promising in adequate lighting conditions; however, it was failed to validate face and gender during moonlight conditions. In [16], the study examined a hieratical approach for a multiview facial recognition to reach the target gender. To serve voting application schemes, the study multiplied images from different viewpoints and created a valuable dataset and enhanced the evaluation outputs.…”

Section: Related Workmentioning

confidence: 99%

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Image-Based Gender Prediction Model Using Multilayer Feed-Forward Neural Networks

Elmahi¹,

Zayid²

2019

IJMIP

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In this study, we develop a reliable and highperformance multi-layer feed-forward artificial neural networks (MFANNs) model for predicting gender classification. The study used features for a set of 450 images randomly chosen from the FERET dataset. We extract the only high-merit candidate parameters form the FERET dataset. A discrete cosine transformation (DCT) is employed to facilitate an image description and conversion. To reach the final gender estimation model, authors examined three artificial neural classifiers and each extremely performs deep computation processes. In addition to the MFANNs, artificial neural networks (ANNs) classifiers include support vector regression with radial-basis function (SVR-RBF) and k-Nearest Neighbor (k-NN). A 10-folds cross-validation technique (CV) is used to prove the integrity of the dataset inputs and enhance the calculation process of the model. In this model, the performance criteria for accuracy rate and mean squared error (MSE) are carried out. Results of the MFANNs models are compared with the ones that obtained by SVR-RBF and k-NN. It is shown that the MFANNs model performs better (i.e. lowest MSE = 0.0789, and highest accuracy rate = 96.9%) than SVR-based and k-NN models. Linked the study findings with the results obtained in the literature review, we conclude that our method achieves a recommended calculation for gender prediction.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Image-Based Gender Prediction Model Using Multilayer Feed-Forward Neural Networks

Elmahi¹,

Zayid²

2019

IJMIP

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“…These cameras are used in both real-time vehicles and simulators to get the performance of the proposed Hypo-Driver system. Since the driver's face is moving in different directions during driving, so we deployed a multiview camera [50], instead of a single-view camera, to implement the five-stage hypovigilance detection system. A visual example of 3-D cameras is shown in Fig.…”

Section: Behavioral Features Extraction and Selection Modulementioning

confidence: 99%

Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System

Riquelme¹,

Olivares²,

Mu駉z³

et al. 2022

Computers, Materials &Amp; Continua

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Traffic accidents are caused by driver fatigue or distraction in many cases. To prevent accidents, several low-cost hypovigilance (hypo-V) systems were developed in the past based on a multimodal-hybrid (physiological and behavioral) feature set. Similarly in this paper, real-time driver inattention and fatigue (Hypo-Driver) detection system is proposed through multi-view cameras and biosignal sensors to extract hybrid features. The considered features are derived from non-intrusive sensors that are related to the changes in driving behavior and visual facial expressions. To get enhanced visual facial features in uncontrolled environment, three cameras are deployed on multiview points (0 • , 45 • , and 90 • ) of the drivers. To develop a Hypo-Driver system, the physiological signals (electroencephalography (EEG), electrocardiography (ECG), electro-myography (sEMG), and electrooculography (EOG)) and behavioral information (PERCLOS70-80-90%, mouth aspect ratio (MAR), eye aspect ratio (EAR), blinking frequency (BF), head-titled ratio (HT-R)) are collected and pre-processed, then followed by feature selection and fusion techniques. The driver behaviors are classified into five stages such as normal, fatigue, visual inattention, cognitive inattention, and drowsy. This improved hypo-Driver system utilized trained behavioral features by a convolutional neural network (CNNs), recurrent neural network and long short-term memory (RNN-LSTM) model is used to extract physiological features. After fusion of these features, the Hypo-Driver system is classified hypo-V into five stages based on trained layers and dropout-layer in the deep-residual neural network (DRNN) model. To test the performance of a hypo-Driver system, data from 20 drivers are acquired. The results of Hypo-Driver compared to state-of-theart methods are presented. Compared to the state-of-the-art Hypo-V system, on average, the Hypo-Driver system achieved a detection accuracy (AC) of 96.5%. The obtained results indicate that the Hypo-Driver system based on multimodal and multiview features outperforms other state-of-the-art driver Hypo-V systems by handling many anomalies.

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Emotion recognition from geometric fuzzy membership functions

Priya

2019

Multimed Tools Appl

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Multi-view face recognition from single RGBD models of the faces

Abstract: This paper is NOT THE PUBLISHED VERSION; but the author's final, peer-reviewed manuscript. The published version may be accessed by following the link in th citation below.

Cited by 8 publications

References 83 publications

Image-Based Gender Prediction Model Using Multilayer Feed-Forward Neural Networks

Image-Based Gender Prediction Model Using Multilayer Feed-Forward Neural Networks

Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System

Emotion recognition from geometric fuzzy membership functions

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