Hand Phone Open-Air Face Image Retrieval for Varying Focal Length and Occlusion from Cloud Storage

Abstract

The rapid growth of smartphone instrument apps like fluke thermal app, medical device apps replaces the existing instruments; in such scenario, the smartphone camera plays a vital role in replacing vision instruments. The smartphone camera image and image retrieval needs effective algorithms compete to the existing algorithms. In this paper, I evaluate the different smartphone images for enhanced face image retrieval. The smartphone face image retrieval id developed with multiple facial and embedded sparse code words attributes for better results of the images acquired under different environmental conditions such as indoor, outdoor, illumination. The 50 images are acquired from four different smartphone face images and publicly available datasets such as PubFig to compare the efficiency of proposed algorithm. From the comparison, dynamic indexing in the algorithm retrieves the image from database in milliseconds. From the results, I conclude that the performance of algorithm varied for smartphone and dataset images.