Literature review of image compression effects on face recognition

In this research work, a literature review is conducted to assess the progress made in the field of image compression effects on the face recognition. The DCT algorithms are considered for the review and their application is limited only to JPEG compression. In this review, progress made in the DCT algorithms of a single image, and a series images from a video, namely 2D DCT and 3D DCT respectively, along with several other algorithms in the application of face recognition are discussed in detail.&nbsp

Effective evaluation of privacy protection techniques in visible and thermal imagery

Privacy protection may be defined as replacing the original content in an image region with a new (less intrusive) content having modified target appearance information to make it less recognizable by applying a privacy protection technique. Indeed the development of privacy protection techniques needs also to be complemented with an established objective evaluation method to facilitate their assessment and comparison. Generally, existing evaluation methods rely on the use of subjective judgements or assume a specific target type in image data and use target detection and recognition accuracies to assess privacy protection. This paper proposes a new annotation-free evaluation method that is neither subjective nor assumes a specific target type. It assesses two key aspects of privacy protection: protection and utility. Protection is quantified as an appearance similarity and utility is measured as a structural similarity between original and privacy-protected image regions. We performed an extensive experimentation using six challenging datasets (having 12 video sequences) including a new dataset (having six sequences) that contains visible and thermal imagery. The new dataset is made available online for community. We demonstrate effectiveness of proposed method by evaluating six image-based privacy protection techniques, and also show comparisons of proposed method over existing methods

Line pattern retrieval using relational histograms

This paper presents a new compact shape representation for retrieving line-patterns from large databases. The basic idea is to exploit both geometric attributes and structural information to construct a shape histogram. We realize this goal by computing the N-nearest neighbor graph for the lines-segments for each pattern. The edges of the neighborhood graphs are used to gate contributions to a two-dimensional pairwise geometric histogram. Shapes are indexed by searching for the line-pattern that maximizes the cross correlation of the normalized histogram bin-contents. We evaluate the new method on a database containing over 2,500 line-patterns each composed of hundreds of lines