基于OpenCV组合优化的人脸识别应用平台设计

人脸识别是最符合人的本能且便捷的识别认证方式,在人工智能领域广泛应用,多数的人脸识别的算法很容易受到外界因素的影响,或者要求识别者站在某一固定位置,识别缓慢和准确率不高。本设计实现了一种基于Open CV的人脸识别的应用平台,首先对人的脸部图像进行采集和预处理,通过对算法优化,利用平台的Eigenfaces、Fisherfaces和LBP(local binary patterns histograms)三种用于人脸识别的算法协同多重使用,并在判定人脸识别系统识别到待检测目标的同时再加上限制条件,再结合Qt框架搭建用户界面,实现人脸模块训练和人脸识别的功能。经过测试,系统界面友好,运行稳定,对人脸位置和环境光照变化具有较好鲁棒性,能快速和准确地对人脸实时检测和识别。

Design of Face Recognition Application Platform Based on OpenCV Combination Optimization

Face recognition is the most instinctive and convenient way of recognition and authentication. It is widely used in the field of artificial intelligence. Most face recognition algorithms are easily affected by external factors, or require the recognizer to stand in a fixed position, and the recognition rate is slow and low. An application platform for face recognition based on OpenCV is designed and implemented. Firstly, human face image is collected and preprocessed. Through optimization of the algorithm, three algorithms for face recognition, Eigenfaces, Fisherfaces and LBP, are used in collaboration and multiplexing. At the same time, restrictions are added to determine the target to be detected in face recognition system, and Qt is combined. The framework builds user interface, realizes the function of face module training and face recognition. After testing, the system has friendly interface, stable operation, good robustness to face location and environmental illumination changes, and can quickly and accurately detect and recognize face in real time.