Automatic face recognition system based on the SIFT features. (August 2015)

• Record Type: Journal Article
• Authors: Lenc, Ladislav; Král, Pavel
• Is Part Of: Computers & electrical engineering. Volume 46(2015)
• Page Start: 256
• Page End: 272
• Publication Date: 2015-08
• Subjects: Face recognition -- Face detection -- Czech News Agency -- Corpus creation -- Confidence measure -- Scale Invariant Feature Transform (SIFT); Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.01.014
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: We proposed and implemented a new face corpus creation algorithm. We created a new facial corpus from the data of the Czech News Agency. We evaluated a novel face recognition method, the SIFT based Kepenekci approach. We proposed and evaluated two novel confidence measure techniques. We proposed, implemented and evaluated the fully automatic face recognition system. Abstract: The main goal of this paper is to propose and implement an experimental fully automatic face recognition system which will be used to annotate photographs during insertion into a database. Its main strength is to successfully process photos of a great number of different individuals taken in a totally uncontrolled environment. The system is available for research purposes for free. It uses our previously proposed SIFT based Kepenekci approach for the face recognition, because it outperforms a number of efficient face recognition approaches on three large standard corpora (namely FERET, AR and LFW). The next goal is proposing a new corpus creation algorithm that extracts the faces from the database and creates a facial corpus. We show that this algorithm is beneficial in a preprocessing step of our system in order to create good quality face models. We further compare the performance of our SIFT based Kepenekci approach with the original Kepenekci method on the created corpus. This comparison proves that our approach significantly outperforms the original one. The last goal is to propose two novel supervised confidence measure methods based on a posterior class probability and a multi-layer perceptron to identify incorrectly recognized faces. These faces are then removed from the recognition results. We experimentally validated that the proposed confidence measures are very efficient and thus suitable for our task.