In todays world, where the need for security is paramount and biometric access control systems are gaining mass acceptance due to their increased reliability, research in this area is quite relevant. Also with the advent of IOT devices and increased community support for cheap and small computers like Raspberry Pi its convenient than ever to design a complete standalone system for any purpose. This paper proposes a Facial Biometric System built on the client-server paradigm using Raspberry Pi 3 model B running a novel local descriptor based parallel algorithm. This paper also proposes an extended version of Local Gradient Hexa Pattern with improved accuracy. The proposed extended version of LGHP improved performance as shown in performance analysis. Extended LGHP shows improvement over other state-of-the-art descriptors namely LDP, LTrP, MLBP and LVP on the most challenging benchmark facial image databases, i.e. Cropped Extended Yale-B, CMU-PIE, color-FERET, LFW, and Ghallager database. Proposed system is also compared with various patents having similar system design and intent to emphasize the difference and novelty of the system proposed.

中文翻译：

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基于扩展LGHP算法的Raspberry Pi面部生物识别系统

在当今世界，对安全性的需求至关重要，而生物识别访问控制系统由于其可靠性的提高而得到了广泛认可，因此在这一领域的研究非常重要。此外，随着物联网设备的出现以及对Raspberry Pi等廉价和小型计算机的社区支持的增加，以任何目的设计一个完整的独立系统都比以往更加方便。本文提出了一种基于Raspberry Pi 3模型B的基于客户端-服务器范例的面部生物识别系统，该系统运行了一种新颖的基于本地描述符的并行算法。本文还提出了具有改进精度的局部渐变六边形图案的扩展版本。如性能分析所示，提议的LGHP扩展版本改善了性能。扩展的LGHP与其他最新的描述符LDP，LTrP，在最具挑战性的基准人脸图像数据库上使用MLBP和LVP，即Cropped Extended Yale-B，CMU-PIE，color-FERET，LFW和Ghallager数据库。还将所提出的系统与具有相似系统设计且旨在强调所提出系统的差异和新颖性的各种专利进行比较。