Improving Audience Analysis System Using Face Image Quality Assessment

2017 ◽  
pp. 175-203 ◽  
Author(s):  
Vladimir Khryashchev ◽  
Alexander Ganin ◽  
Ilya Nenakhov ◽  
Andrey Priorov
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Face Image ◽  
Audience Analysis ◽  
Analysis System

scholarly journals Face Image Quality Assessment: A Literature Survey

2022 ◽  
Author(s):  
Torsten Schlett ◽  
Christian Rathgeb ◽  
Olaf Henniger ◽  
Javier Galbally ◽  
Julian Fierrez ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Face Image ◽  
Quality Data ◽  
Learning Approaches ◽  
The Face ◽  
Future Work

The performance of face analysis and recognition systems depends on the quality of the acquired face data, which is influenced by numerous factors. Automatically assessing the quality of face data in terms of biometric utility can thus be useful to detect low-quality data and make decisions accordingly. This survey provides an overview of the face image quality assessment literature, which predominantly focuses on visible wavelength face image input. A trend towards deep learning based methods is observed, including notable conceptual differences among the recent approaches, such as the integration of quality assessment into face recognition models. Besides image selection, face image quality assessment can also be used in a variety of other application scenarios, which are discussed herein. Open issues and challenges are pointed out, i.a. highlighting the importance of comparability for algorithm evaluations, and the challenge for future work to create deep learning approaches that are interpretable in addition to providing accurate utility predictions.

DFQA: Deep Face Image Quality Assessment

2019 ◽  
pp. 655-667
Author(s):  
Fei Yang ◽  
Xiaohu Shao ◽  
Lijun Zhang ◽  
Pingling Deng ◽  
Xiangdong Zhou ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Face Image

An inspired haxby brain perceptual model for facial images quality assessment

2020 ◽  
Vol 39 (6) ◽  
pp. 8543-8555
Author(s):  
Azamossadat Nourbakhsh ◽  
Mohammad-Shahram Moin ◽  
Arash Sharifi
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Structural Information ◽  
Face Image ◽  
Assessment System ◽  
Civil Aviation ◽  
Information Encoding ◽  
Human Quality ◽  
Verification Systems

Face is the most important and most popular biometric used in many identification and verification systems. In these systems, for reducing recognition error rate, the quality of input images need to be as high as possible. Face Image Compliancy verification (FICV) is one of the most essential methods for this purpose. In this research, a brain functionality inspired model is presented for FICV using Haxby model, which is a face visual perception consistent model containing three bilateral areas for three different functionalities. As a result, contribution of this work is presenting a new model, based on human brain functionality, improving the compliancy verification of face images in FICV context. Perceptual understanding of an image is the motivation of most of the quality assessment methods, i.e., the human quality perception is considered as a gold standard and a perfect reference for recognition and quality assessment. The model presented in this work aims to make the operational process of face image quality assessment system closer to the performance of a human expert. Three basic modules have been introduced. Face structural information, for initial information encoding, is simulated by an extended Viola-Jones model. Face image quality assessment is presented by International Civil Aviation Organization (ICAO), in ICAO (ISO / IEC19794 -11) requirements’ compliancy assessment document. Like Haxby model, perception is performed through two distinct functional and neurological pathways, using Hierarchical Maximum pooling (HMAX) and Convolutional Deep Belief Networks (CDBN). Information storing and fetching for training are similar to their corresponding modules in brain. For simulating the brain decision making, the final results of two separate paths are integrated by weighting sum operator. Nine ISO / ICAO requirements were used for testing the model. The simulation results, using AR and PUT databases, shows improvements in six requirements using the proposed method, in comparison with the FICV benchmark.

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