An Analysis to Identify the Importance of Visual Face Features and Ranking

Author(s):  
Mohd. Aamir Khan ◽  
Anand Singh Jalal
Keyword(s):  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yikui Zhai ◽  
He Cao ◽  
Wenbo Deng ◽  
Junying Gan ◽  
Vincenzo Piuri ◽  
...  

Because of the lack of discriminative face representations and scarcity of labeled training data, facial beauty prediction (FBP), which aims at assessing facial attractiveness automatically, has become a challenging pattern recognition problem. Inspired by recent promising work on fine-grained image classification using the multiscale architecture to extend the diversity of deep features, BeautyNet for unconstrained facial beauty prediction is proposed in this paper. Firstly, a multiscale network is adopted to improve the discriminative of face features. Secondly, to alleviate the computational burden of the multiscale architecture, MFM (max-feature-map) is utilized as an activation function which can not only lighten the network and speed network convergence but also benefit the performance. Finally, transfer learning strategy is introduced here to mitigate the overfitting phenomenon which is caused by the scarcity of labeled facial beauty samples and improves the proposed BeautyNet’s performance. Extensive experiments performed on LSFBD demonstrate that the proposed scheme outperforms the state-of-the-art methods, which can achieve 67.48% classification accuracy.


Cortex ◽  
2015 ◽  
Vol 72 ◽  
pp. 115-123 ◽  
Author(s):  
Gal Namdar ◽  
Galia Avidan ◽  
Tzvi Ganel

2013 ◽  
Vol 753-755 ◽  
pp. 2941-2944
Author(s):  
Ming Hui Zhang ◽  
Yao Yu Zhang

Seeing that human face features are unique, an increasing number of face recognition algorithms on existing ATM are proposed. Since face detection is a primary link of face recognition, our system adopts AdaBoost algorithm which is based on face detection. Experiment results demonstrated that the computing time of face detection using this algorithm is about 70ms, and the single and multiple human faces can be effectively measured under well environment, which meets the demand of the system.


2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Muhammad Sajid ◽  
Nouman Ali ◽  
Saadat Hanif Dar ◽  
Naeem Iqbal Ratyal ◽  
Asif Raza Butt ◽  
...  

Recently, face datasets containing celebrities photos with facial makeup are growing at exponential rates, making their recognition very challenging. Existing face recognition methods rely on feature extraction and reference reranking to improve the performance. However face images with facial makeup carry inherent ambiguity due to artificial colors, shading, contouring, and varying skin tones, making recognition task more difficult. The problem becomes more confound as the makeup alters the bilateral size and symmetry of the certain face components such as eyes and lips affecting the distinctiveness of faces. The ambiguity becomes even worse when different days bring different facial makeup for celebrities owing to the context of interpersonal situations and current societal makeup trends. To cope with these artificial effects, we propose to use a deep convolutional neural network (dCNN) using augmented face dataset to extract discriminative features from face images containing synthetic makeup variations. The augmented dataset containing original face images and those with synthetic make up variations allows dCNN to learn face features in a variety of facial makeup. We also evaluate the role of partial and full makeup in face images to improve the recognition performance. The experimental results on two challenging face datasets show that the proposed approach can compete with the state of the art.


Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1245 ◽  
Author(s):  
Tao Wang ◽  
Wen Wang ◽  
Hui Liu ◽  
Tianping Li

With the revolutionary development of cloud computing and internet of things, the integration and utilization of “big data” resources is a hot topic of the artificial intelligence research. Face recognition technology information has the advantages of being non-replicable, non-stealing, simple and intuitive. Video face tracking in the context of big data has become an important research hotspot in the field of information security. In this paper, a multi-feature fusion adaptive adjustment target tracking window and an adaptive update template particle filter tracking framework algorithm are proposed. Firstly, the skin color and edge features of the face are extracted in the video sequence. The weighted color histogram are extracted which describes the face features. Then we use the integral histogram method to simplify the histogram calculation of the particles. Finally, according to the change of the average distance, the tracking window is adjusted to accurately track the tracking object. At the same time, the algorithm can adaptively update the tracking template which improves the accuracy and accuracy of the tracking. The experimental results show that the proposed method improves the tracking effect and has strong robustness in complex backgrounds such as skin color, illumination changes and face occlusion.


2021 ◽  
Author(s):  
Alice Gomez ◽  
Guillaume Lio ◽  
Manuela Costa ◽  
Angela Sirigu ◽  
Caroline Demily

Abstract Williams syndrome (WS) and Autism Spectrum Disorders (ASD) are psychiatric conditions associated with atypical but opposite face-to-face interactions patterns: WS patients overly stare at others, ASD individuals escape eye contact. Whether these behaviors result from dissociable visual processes within the occipito-temporal pathways is unknown. Using high-density electroencephalography, multivariate pattern classification and group blind source separation, we searched for face-related neural signals that could best discriminate WS (N = 14), ASD (N = 14) and neurotypical populations (N = 14). We found two peaks in neurotypical participants: the first at 170ms, an early signal known to be implicated in low-level face features, the second at 260ms, a late component implicated in decoding salient face social cues. The late 260ms signal varied as a function of the distance of the eyes in the face stimulus with respect to the viewers’ fovea, meaning that it was strongest when the eyes were projected on the fovea and weakest when projected in the retinal periphery. Remarkably, both components were found distinctly impaired and preserved in WS and ASD. In WS, we could weakly decode the 170ms signal probably due to their relatively poor ability to process faces’ morphology while the late 260ms component shown to be eye sensitive was highly significant. The reverse pattern was observed in ASD participants who showed neurotypical like early 170ms evoked activity but impaired late evoked 260ms signal. Our study reveals a dissociation between WS and ASD patients and point at different neural origins for their social impairments.


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