Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advanced compiler design and implementation
Advanced compiler design and implementation
Source code optimization and profiling of energy consumption in embedded systems
ISSS '00 Proceedings of the 13th international symposium on System synthesis
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Storage Management Programmable Process
Storage Management Programmable Process
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Data Reuse Exploration Techniques for Loop-Dominated Applications
Proceedings of the conference on Design, automation and test in Europe
Embedded Hardware Face Detection
VLSID '04 Proceedings of the 17th International Conference on VLSI Design
Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast and robust face detection based on module switching network
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Simplifying convnets for fast learning
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
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A high-level optimization methodology is applied for implementing the well-known convolutional face finder (CFF) algorithm for real-time applications on mobile phones, such as teleconferencing, advanced user interfaces, image indexing, and security access control. CFF is based on a feature extraction and classification technique which consists of a pipeline of convolutions and subsampling operations. The design of embedded systems requires a good trade-off between performance and code size due to the limited amount of available resources. The followed methodology copes with the main drawbacks of the original implementation of CFF such as floating-point computation and memory allocation, in order to allow parallelism exploitation and perform algorithm optimizations. Experimental results show that our embedded face detection system can accurately locate faces with less computational load and memory cost. It runs on a 275 MHz Starcore DSP at 35 QCIF images/s with state-of-the-art detection rates and very low false alarm rates.