Face recognition has become a fundamental biometric tool that ensures identification of people. Besides a high computational cost, it constitutes an open problem for identifying faces under ideal conditions as well as those under general conditions. Though the advent of high memory and inexpensive computer technologies has made the implementation of face recognition possible in several devices and authentication systems, achieving $$100\%$$ face recognition in real time is still a challenging task. This paper implements an evolutionary computer genetic algorithm for optimizing the number of interest points on faces, intended to get a quick and precise facial recognition using local analysis texture technique applied to CBIR methodology. Our approach was evaluated using different databases, getting an efficient facial recognition of up to $$100\%$$ considering only seven interest points from a total of 54 cited in the literature. The interest points reduction was possible through a parallel implementation of our approach using a 54-processor cluster that executes the similar task up to $$300\%$$ more faster.

中文翻译：

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使用进化算法和 CBIR 进行人脸识别的兴趣点减少

人脸识别已成为一种基本的生物识别工具，可确保识别人员。除了高计算成本外，它构成了在理想条件下以及一般条件下识别人脸的开放问题。尽管高内存和廉价计算机技术的出现使得在多种设备和身份验证系统中实现人脸识别成为可能，但实时实现 100 美元\%$$ 的人脸识别仍然是一项具有挑战性的任务。本文实现了一种用于优化面部兴趣点数量的进化计算机遗传算法，旨在使用应用于 CBIR 方法的局部分析纹理技术获得快速而精确的面部识别。我们的方法使用不同的数据库进行评估，仅考虑文献中引用的 54 个兴趣点中的 7 个兴趣点，即可获得高达 $100\%$$ 的有效面部识别。通过使用 54 处理器集群并行实现我们的方法，可以减少兴趣点，该集群执行类似任务的速度最高可达 300 美元\%$$。