Human face detection is often the first step in applications such as video surveillance, human computer interface, face recognition, and image database management. We propose a face detection algorithm for color images in the presence of varying lighting conditions as well as complex backgrounds. Our method detects skin regions over the entire image, and then generates face candidates based on the spatial arrangement of these skin patches. The algorithm constructs eye, mouth, and boundary maps for verifying each face candidate. Experimental results demonstrate successful detection over a wide variety of facial variations in color, position, scale, rotation, pose, and expression from several photo collections.
With the wide spread utilization of biometric idenrification systems, establishing rhe uuthenticiq of biometric data itself has emerged as an important research issue. We present a fingerprint image watermarking method rhat can embed facial ii$ormation into host fingerprint images. This scheme has the advantage that in addition to fingerprint matching. the recovered face during the decoding can be used to establish the authenticin. of the fingerprint and rhe user. By computing the ROC curves on afingerprint database of 160 individuals, we show the advantages of the proposed watermarking scheme. Further, our scheme does not introduce any signifirant degradation in the fingerprint matching performance.
Accurate and robust tracking of humans is of growing interest in the inrage processing and computer vision communiries. The a b i l i h of a vision system f o track the subjects and accurately predict their future locations is critical to man? surveillance and camera control applications. Further, an inference of the ppe of motion as 4 1 as to rapidly detect and switch between morion models is critical since in some applications the switching time between motion models can be extremely small. The Interacting Miilriple Model (IMM) Kalnum filter provides a powerful f r a m e w r k for performing the tracking of both the motion as well as the shape of these subjects. The tracking q s t e m utilizes a simple geometric shape primirive such as an ellipse to define a bounding extent of the subject. The utili0 of the IMM paradigm for rapid model switching and behaviour detecrion is shown for a passenger airbag suppression system in an automobile. The simplicity of rhe tnerhods and the robusrness of the underlying IMMfilterIng make the framework well suited for low-cost embedded real-time motion sequence analysis systems.
Abstract. Current appearance-based face recognition system encounters the difficulty to recognize faces with appearance variations, while only a small number of training images are available. We present a scheme based on the analysis by synthesis framework. A 3D generic face model is aligned onto a given frontal face image. A number of synthetic face images are generated with appearance variations from the aligned 3D face model. These synthesized images are used to construct an affine subspace for each subject. Training and test images for each subject are represented in the same way in such a subspace. Face recognition is achieved by minimizing the distance between the subspace of a test subject and that of each subject in the database. Only a single face image of each subject is available for training in our experiments. Preliminary experimental results are promising.
Human face detection is often the first step in applications such as video surveill:ance, human computer interface, face recognition, and image database management.We propose a face detection algorithm for color images in the presence of varying lighting conditions as well as complex backgrounds. Our method detects skin regions over the entire image, and then generates face candidates based on the spatial arrangement of these skin patches. The algorithm constructs eye, mouth, and boundary maps for verifying each face candidate. Experimental results demonstrate successful detection over a wide variety of facial variations in color, position, scale, rotation, pose, and expression from several photo collections.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.