Automatic and Adaptive Red Eye Detection and Removal: Investigation and Implementation
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Redeye artifact is the most prevalent problem in the flash photography, especially using compact cameras with built-in flash, which bothers both amateur and professional photographers. Hence, removing the affected redeye pixels has become an important skill. This thesis work presents a completely automatic approach for the purpose of redeye detection and removal and it consists of two modules: detection and correction of the redeye pixels in an individual eye, detection of two red eyes in an individual face.This approach is considered as a combination of some of the previous attempts in the area of redeye removal together with some minor and major modifications and novel ideas. The detection procedure is based on the redness histogram analysis followed by two adaptive methods, general and specific approaches, in order to find a threshold point. The correction procedure is a four step algorithm which does not solely rely on the detected redeye pixels. It also applies some more pixel checking, such as enlarging the search area and neighborhood checking, to improve the reliability of the whole procedure by reducing the image degradation risk. The second module is based on a skin-likelihood detection algorithm. A completely novel approach which is utilizing the Golden Ratio in order to segment the face area into some specific regions is implemented in the second module. The proposed method in this thesis work is applied on more than 40 sample images; by considering some requirements and constrains, the achieved results are satisfactory.
Place, publisher, year, edition, pages
2012. , 65 p.
Red eye detection, red eye correction, skin color model, face detection
IdentifiersURN: urn:nbn:se:liu:diva-77977ISRN: LiU-ITN-TEK-A--12/029--SEOAI: oai:DiVA.org:liu-77977DiVA: diva2:530360
Subject / course
Graphic Design and Communication
2012-05-16, Norrköping, 10:15 (English)