The standard multi-scale, multi-orientation Gabor filter ensemble (SGFE) in the face recognition task reposits 40 filters localized in 8 orientations and 5 scales, with a real and an imaginary constituent. This paper devises a simple means of filter diversification, dubbed as multi-fold Gabor filter convolution (-FGFC), where a set of pre-selected filters, e.g., single-scale Gabor filters across varying orientations, are self-cross convolved by folds to instantiate the offspring filters. To facilitate filter selection for-FGFC, this paper summarizes SGFE into the condensed Gabor filter ensemble (CGFE) of only 8 filters. In addition, an average histogram pooling operator is proposed to downsample and regulate the demodulated Gabor phase features prior to the final compression stage. The performance of a specific M-FGFC instance, i.e., the 2-FGFC descriptor, is investigated on FERET I (frontal), FERET II (nonfrontal) and AR datasets. The experimental results on FERET I substantiates that the 2-FGFC descriptor outperforms the leading state of the art face descriptors.
|Title of host publication||2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2016 May 18|
|Event||41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China|
Duration: 2016 Mar 20 → 2016 Mar 25
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Other||41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016|
|Period||16/3/20 → 16/3/25|
Bibliographical notePublisher Copyright:
© 2016 IEEE.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering