A DSP-based approach for the implementation of face recognition algorithms

Batur, Aziz Umit; Flinchbaugh, Bruce E.; Hayes, M.H.

doi:10.1109/icassp.2003.1202342

Cited by 14 publications

(9 citation statements)

References 7 publications

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“…For instance, in [11], it was found that floatingpoint computations were the major bottleneck toward reaching a real-time performance. The optimization strategy employed was to convert only the most computationally intensive parts of the algorithm over to fixed-point to ease the coding effort while still achieving a significant speedup.…”

Section: Fixed-point Computationsmentioning

confidence: 99%

“…Lately, digital video surveillance systems have become a high-priority topic of research worldwide [6,16,36,37,40,45,69,98,149,155]. Relevant technologies include automatic, robust face recognition [11,28,92,112,146], gesture recognition [111,142], tracking of human or object movement [9,40,61,68,76,92,102,151], distributed or networked video surveillance with multiple cameras [17,37,53,75], etc. Such systems can be categorized as being hard real-time systems and require one to address some difficult problems when deployed in realworld environments with varying lighting conditions.…”

Section: Growth In Applications Of Real-time Image/video Processingmentioning

confidence: 99%

“…In [146], to meet the real-time performance requirements, input images of size 320 × 240 were downscaled to 80 × 60 to help speed up the discussed face recognition system. Similarly, in [11], input images of size 640 × 480 were downsized to 128 × 128 to help speed up the subsequent processing.…”

Section: Subsamplingmentioning

confidence: 99%

See 2 more Smart Citations

Real-Time Image and Video Processing: From Research to Reality

Kehtarnavaz¹,

Gamadia²

2006

Synthesis Lectures on Image, Video, and Multimedia Processing

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This book presents an overview of the guidelines and strategies for transitioning an image or video processing algorithm from a research environment into a real-time constrained environment. Such guidelines and strategies are scattered in the literature of various disciplines including image processing, computer engineering, and software engineering, and thus have not previously appeared in one place. By bringing these strategies into one place, the book is intended to serve the greater community of researchers, practicing engineers, industrial professionals, who are interested in taking an image or video processing algorithm from a research environment to an actual real-time implementation on a resource constrained hardware platform. These strategies consist of algorithm simplifications, hardware architectures, and software methods. Throughout the book, carefully selected representative examples from the literature are presented to illustrate the discussed concepts. After reading the book, the readers are exposed to a wide variety of techniques and tools, which they can then employ for designing a real-time image or video processing system of interest. KEYWORDSReal-time image and video processing, Real-time implementation strategies, Algorithmic simplifications for real-time image and video processing, Hardware platforms for real-time image and video processing, Software methods for real-time image and video processing v

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Section: Fixed-point Computationsmentioning

confidence: 99%

Section: Growth In Applications Of Real-time Image/video Processingmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Image and Video Processing: From Research to Reality

Kehtarnavaz¹,

Gamadia²

2006

Synthesis Lectures on Image, Video, and Multimedia Processing

View full text Add to dashboard Cite

show abstract

“…There are two DSP-based AFR system implementations by Batur, Flinchbaugh, and Hayes (2003) and Wei and Bigdeli (2004) that are reported in the literature.…”

Section: Dsp-based Implementationsmentioning

confidence: 99%

“…The implementation by Batur, et al (2003) consists of four stages: face detection, face feature localization, face normalization, and face recogni-tion. The probabilistic visual learning algorithm proposed by Moghaddam and Pentland (1997) is used for face detection.…”

Section: Dsp-based Implementationsmentioning

confidence: 99%

Robust Face Recognition Technique for a Real-Time Embedded Face Recognition System

Shan

Bigdeli

Lovell

et al. 2008

Pattern Recognition Technologies and Applications

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In this chapter, we propose a pose variability compensation technique, which synthesizes realistic frontal face images from nonfrontal views. It is based on modeling the face via active appearance models and estimating the pose through a correlation model. The proposed technique is coupled with adaptive principal component analysis (APCA), which was previously shown to perform well in the presence of both lighting and expression variations. The proposed recognition techniques, though advanced, are not computationally intensive. So they are quite well suited to the embedded system environment. Indeed, the authors have implemented an early prototype of a face recognition module on a mobile camera phone so the camera can be used to identify the person holding the phone.

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Smart Camera Systems Technology Roadmap

Flinchbaugh

Real-Time Vision for Human-Computer Interaction

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A DSP-based approach for the implementation of face recognition algorithms

Cited by 14 publications

References 7 publications

Real-Time Image and Video Processing: From Research to Reality

Real-Time Image and Video Processing: From Research to Reality

Robust Face Recognition Technique for a Real-Time Embedded Face Recognition System

Smart Camera Systems Technology Roadmap

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