A new approach for matching of face sketch images with face photo images and vice versa has been presented here. For the extraction of local edge features from both the sketch and photo images, a new local feature called local gradient checksum (LGCS) has been developed. LGCS is a modality reduction local edge feature on gradient domain. It is calculated as the summation of four pairs of gradient differences between two local pixels that are at 180° with each other. The Euclidean distance between query sketch and gallery of photos are measured depending on extracted LGCS features. To further improve the result, a multi-scale LGCS is proposed. A rank-1 accuracy of 100 % is achieved in a gallery of 606 photos consisting of CUHK, AR, and XM2VTS face dataset. The proposed face sketch-photo recognition system requires neither learning procedures nor training data. Further, the experiment is extended to test the robustness of the proposed algorithm on blurred, noisy and disguised sketches, as well as photos. Under those situations also, LGCS has outperformed center-symmetric local binary pattern, directional local extrema pattern and weber local descriptor feature extraction techniques.
International Journal of Machine Learning and Cybernetics – Springer Journals
Published: Mar 14, 2016
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera