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.

中文翻译：

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一个启发性的哈克斯比大脑感知模型，用于面部图像质量评估

面部识别是许多识别和验证系统中使用的最重要和最流行的生物识别技术。在这些系统中，为了降低识别错误率，输入图像的质量需要尽可能高。人脸图像兼容性验证（FICV）是用于此目的的最重要方法之一。在这项研究中，使用Haxby模型为FICV提出了一个受大脑功能启发的模型，该模型是面部视觉感知一致模型，其中包含针对三个不同功能的三个双边区域。结果，这项工作的成果是提出了一种基于人脑功能的新模型，从而改进了FICV环境下人脸图像的合规性验证。对图像的感知理解是大多数质量评估方法的动机，即 人体质量感知被视为黄金标准，是识别和质量评估的完美参考。这项工作中提出的模型旨在使人脸图像质量评估系统的操作过程更接近人类专家的性能。引入了三个基本模块。通过扩展的Viola-Jones模型模拟用于初始信息编码的人脸结构信息。人脸图像质量评估是由国际民航组织（ICAO）在国际民航组织（ISO / IEC19794 -11）要求的符合性评估文件中提出的。像Haxby模型一样，感知是通过两个不同的功能和神经系统途径执行的，分别使用分层最大池（HMAX）和卷积深度信念网络（CDBN）。用于训练的信息存储和获取类似于它们在大脑中的相应模块。为了模拟大脑决策，通过加权和运算符将两条单独路径的最终结果进行积分。该模型使用了9个ISO / ICAO要求。使用AR和PUT数据库进行的仿真结果显示，与FICV基准相比，使用所提出的方法可以改善六项要求。