Unconstrained Band-pass Optimized Correlation Filter (UBoCF): An application to face recognition

Achuthanunni, Amith; Saha, Ratul Kishore; Banerjee, Pradipta

doi:10.1109/aspcon.2018.8748292

2018 IEEE Applied Signal Processing Conference (ASPCON) 2018

DOI: 10.1109/aspcon.2018.8748292

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Unconstrained Band-pass Optimized Correlation Filter (UBoCF): An application to face recognition

Amith Achuthanunni¹,

Ratul Kishore Saha²,

Pradipta Banerjee³

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2021

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“…Achuthanunni et al [30] and Banerjee et al [31] propose the band-pass pre-processing of Laplacian of Gaussian (LoG) of unconstrained correlation filter for facial recognition. The band-pass filtering achieves a trade-off between suppressing irrelevant details and enhancing the edges for feature representation.…”

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Weight Quantization Retraining for Sparse and Compressed Spatial Domain Correlation Filters

et al. 2021

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Using Spatial Domain Correlation Pattern Recognition (CPR) in Internet-of-Things (IoT)-based applications often faces constraints, like inadequate computational resources and limited memory. To reduce the computation workload of inference due to large spatial-domain CPR filters and convert filter weights into hardware-friendly data-types, this paper introduces the power-of-two (Po2) and dynamic-fixed-point (DFP) quantization techniques for weight compression and the sparsity induction in filters. Weight quantization re-training (WQR), the log-polar, and the inverse log-polar geometric transformations are introduced to reduce quantization error. WQR is a method of retraining the CPR filter, which is presented to recover the accuracy loss. It forces the given quantization scheme by adding the quantization error in the training sample and then re-quantizes the filter to the desired quantization levels which reduce quantization noise. Further, Particle Swarm Optimization (PSO) is used to fine-tune parameters during WQR. Both geometric transforms are applied as pre-processing steps. The Po2 quantization scheme showed better performance close to the performance of full precision, while the DFP quantization showed further closeness to the Receiver Operator Characteristic of full precision for the same bit-length. Overall, spatial-trained filters showed a better compression ratio for Po2 quantization after retraining of the CPR filter. The direct quantization approach achieved a compression ratio of 8 at 4.37× speedup with no accuracy degradation. In contrast, quantization with a log-polar transform is accomplished at a compression ratio of 4 at 1.12× speedup, but, in this case, 16% accuracy of degradation is noticed. Inverse log-polar transform showed a compression ratio of 16 at 8.90× speedup and 6% accuracy degradation. All the mentioned accuracies are reported for a common database.

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Weight Quantization Retraining for Sparse and Compressed Spatial Domain Correlation Filters

et al. 2021

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Unconstrained Band-pass Optimized Correlation Filter (UBoCF): An application to face recognition

Cited by 1 publication

References 27 publications

Weight Quantization Retraining for Sparse and Compressed Spatial Domain Correlation Filters

Weight Quantization Retraining for Sparse and Compressed Spatial Domain Correlation Filters

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