scholarly journals Ear Localization from Side Face Images using Distance Transform and Template Matching

2008 ◽  
Author(s):  
Surya Prakash ◽  
Umarani Jayaraman ◽  
Phalguni Gupta
Keyword(s):  
Template Matching ◽  
Distance Transform ◽  
Face Images ◽  
Side Face

EarLocalizer

2019 ◽  
pp. 137-159
Author(s):  
Aman Kamboj ◽  
Rajneesh Rani ◽  
Aditya Nigam
Keyword(s):  
Environmental Conditions ◽  
Crucial Role ◽  
Healthcare Sector ◽  
Biometric Authentication ◽  
Face Images ◽  
Proposed Model ◽  
In The Wild ◽  
Side Face ◽  
Background Clutter ◽  
Overall Performance

With much concern over security, it has become essential to maintain the identity and track of an individual's activities in the modern healthcare sector. Although there are biometric authentication systems based on different modalities, recognition of a person using the ear has gained much attention as ears are unique. Ear localization is a first step for ear-based biometric authentication systems, and this needs to be accurate, since it plays a crucial role in the overall performance of the system. The localization of ear in the side face images captured in the wild possess great challenges due to varying angles, light, scale, background clutter, blur and occlusion, etc. In this chapter, the authors have proposed EarLocalizer model to localize the ear, which is inspired by Faster-RCNN. The model is evaluated on two wild ear databases, UBEAR-II and USTB-III, and has achieved an accuracy of 95% and 99.08%, respectively, at IOU (Intersection over Union) = 0.5. The results of the proposed model signify that the model is invariant to the environmental conditions.

scholarly journals Audio-Visual Speech Recognition Using Lip Information Extracted from Side-Face Images

2007 ◽  
Vol 2007 ◽  
pp. 1-9 ◽  
Author(s):  
Koji Iwano ◽  
Tomoaki Yoshinaga ◽  
Satoshi Tamura ◽  
Sadaoki Furui
Keyword(s):  
Speech Recognition ◽  
Visual Speech ◽  
Face Images ◽  
Visual Speech Recognition ◽  
Side Face

Multimodal Biometrics Fusion for Human Recognition in Video

2010 ◽  
pp. 414-447
Author(s):  
Xiaoli Zhou ◽  
Bir Bhanu
Keyword(s):  
Recognition System ◽  
Video Frame ◽  
Compact Representation ◽  
Human Recognition ◽  
Face Images ◽  
Face Features ◽  
Video Frames ◽  
Fusion Methods ◽  
Side Face ◽  
Match Score

This chapter introduces a new video based recognition system to recognize noncooperating individuals at a distance in video, who expose side views to the camera. Information from two biometric sources, side face and gait, is utilized and integrated for recognition. For side face, an enhanced side face image (ESFI), a higher resolution image compared with the image directly obtained from a single video frame, is constructed, which integrates face information from multiple video frames. For gait, the gait energy image (GEI), a spatiotemporal compact representation of gait in video, is used to characterize human walking properties. The features of face and gait are extracted from ESFI and GEI, respectively. They are integrated at both of the match score level and the feature level by using different fusion strategies. The system is tested on a database of video sequences, corresponding to 45 people, which are collected over several months. The performance of different fusion methods are compared and analyzed. The experimental results show that (a) the idea of constructing ESFI from multiple frames is promising for human recognition in video and better face features are extracted from ESFI compared to those from the original side face images; (b) the synchronization of face and gait is not necessary for face template ESFI and gait template GEI; (c) integrated information from side face and gait is effective for human recognition in video. The feature level fusion methods achieve better performance than the match score level methods fusion overall.

How re-training process affect the performance of no-reference image quality metric for face images

2019 ◽  
Vol 2019 (5) ◽  
pp. 528-1-528-6
Author(s):  
Xinwei Liu ◽  
Christophe Charrier ◽  
Marius Pedersen ◽  
Patrick Bours
Keyword(s):  
Image Quality ◽  
Reference Image ◽  
Training Process ◽  
Quality Metric ◽  
Face Images ◽  
Image Quality Metric

Gradient Texture Classification Based On Age Prediction of Face Images

2014 ◽  
Vol 1 (3) ◽  
pp. 23-31
Author(s):  
Basava Raju ◽  
◽  
K. Y. Rama Devi ◽  
P. V. Kumar ◽  
◽  
...  
Keyword(s):  
Texture Classification ◽  
Face Images ◽  
Age Prediction
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