Computing feature vectors of students for face recognition using Apache Spark

D. Kariboz, A. Bogdanchikov, K. Orynbekova
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Abstract

With a massive increase of images and videos in the internet, processing these data for face detection and recognition is a bottleneck in current situation. At least there is a lack of frameworks and tools to process lots of data efficiently and not time consuming. System proposed in this paper offers face recognition framework based on Apache Spark to analyze lots images and videos from Instagram. As a people need to be searched and recognized are university students, their feature vectors used for recognition initially computed and can be enlarged at any moment. Spark’s Resilient Distributed Databases gives ability to compute all the intermediate data directly in memory making computations faster meanwhile keeping resilience property.
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利用Apache Spark计算学生的特征向量进行人脸识别
随着互联网上图像和视频的大量增加,处理这些数据进行人脸检测和识别是目前的瓶颈。至少缺乏框架和工具来高效地处理大量数据,而且不耗费时间。本文提出的系统提供了基于Apache Spark的人脸识别框架,用于分析来自Instagram的大量图像和视频。由于需要搜索和识别的人群是大学生,其用于识别的特征向量是初始计算的,并且可以随时放大。Spark的弹性分布式数据库提供了直接在内存中计算所有中间数据的能力,使计算速度更快,同时保持弹性属性。
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