Weighted contourlet binary patterns and image-based fisher linear discriminant for face recognition
We propose a novel face representation model, called the weighted Contourlet binary patterns (WCBP), based on the NonSubsampled Contourlet Transform (NSCT), for face recognition. The decomposition using NSCT can capture rich image information at multiple scales, orientations, and frequency bands. Th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Weifeng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Umfang: |
11 |
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| Übergeordnetes Werk: |
Enthalten in: The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast - Liu, Yang ELSEVIER, 2018, an international journal, Amsterdam |
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| Übergeordnetes Werk: |
volume:267 ; year:2017 ; day:6 ; month:12 ; pages:436-446 ; extent:11 |
| Links: |
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| DOI / URN: |
10.1016/j.neucom.2017.06.045 |
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ELV030579856 |
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| 520 | |a We propose a novel face representation model, called the weighted Contourlet binary patterns (WCBP), based on the NonSubsampled Contourlet Transform (NSCT), for face recognition. The decomposition using NSCT can capture rich image information at multiple scales, orientations, and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each NSCT-decomposed image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image (named as WCBP+FLD) for dimension reduction of features. Our extensive experiments on the public FERET, CAS-PEAL-R1 and LFW databases demonstrate that the non-weighted Contourlet binary patterns performs better than local Gabor binary patterns. WCBP further improves the recognition rates. WCBP+FLD can achieve much competitive or even better recognition performance compared with the state-of-the-art Gabor feature based face recognition methods. | ||
| 520 | |a We propose a novel face representation model, called the weighted Contourlet binary patterns (WCBP), based on the NonSubsampled Contourlet Transform (NSCT), for face recognition. The decomposition using NSCT can capture rich image information at multiple scales, orientations, and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each NSCT-decomposed image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image (named as WCBP+FLD) for dimension reduction of features. Our extensive experiments on the public FERET, CAS-PEAL-R1 and LFW databases demonstrate that the non-weighted Contourlet binary patterns performs better than local Gabor binary patterns. WCBP further improves the recognition rates. WCBP+FLD can achieve much competitive or even better recognition performance compared with the state-of-the-art Gabor feature based face recognition methods. | ||
| 700 | 1 | |a Wang, Yichuan |4 oth | |
| 700 | 1 | |a Xu, Zhen |4 oth | |
| 700 | 1 | |a Jiang, Yinyan |4 oth | |
| 700 | 1 | |a Lu, Zongqing |4 oth | |
| 700 | 1 | |a Liao, Qingmin |4 oth | |
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10.1016/j.neucom.2017.06.045 doi GBV00000000000357.pica (DE-627)ELV030579856 (ELSEVIER)S0925-2312(17)31173-6 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Li, Weifeng verfasserin aut Weighted contourlet binary patterns and image-based fisher linear discriminant for face recognition 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose a novel face representation model, called the weighted Contourlet binary patterns (WCBP), based on the NonSubsampled Contourlet Transform (NSCT), for face recognition. The decomposition using NSCT can capture rich image information at multiple scales, orientations, and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each NSCT-decomposed image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image (named as WCBP+FLD) for dimension reduction of features. Our extensive experiments on the public FERET, CAS-PEAL-R1 and LFW databases demonstrate that the non-weighted Contourlet binary patterns performs better than local Gabor binary patterns. WCBP further improves the recognition rates. WCBP+FLD can achieve much competitive or even better recognition performance compared with the state-of-the-art Gabor feature based face recognition methods. We propose a novel face representation model, called the weighted Contourlet binary patterns (WCBP), based on the NonSubsampled Contourlet Transform (NSCT), for face recognition. The decomposition using NSCT can capture rich image information at multiple scales, orientations, and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each NSCT-decomposed image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image (named as WCBP+FLD) for dimension reduction of features. Our extensive experiments on the public FERET, CAS-PEAL-R1 and LFW databases demonstrate that the non-weighted Contourlet binary patterns performs better than local Gabor binary patterns. WCBP further improves the recognition rates. WCBP+FLD can achieve much competitive or even better recognition performance compared with the state-of-the-art Gabor feature based face recognition methods. Wang, Yichuan oth Xu, Zhen oth Jiang, Yinyan oth Lu, Zongqing oth Liao, Qingmin oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:267 year:2017 day:6 month:12 pages:436-446 extent:11 https://doi.org/10.1016/j.neucom.2017.06.045 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 267 2017 6 1206 436-446 11 |
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10.1016/j.neucom.2017.06.045 doi GBV00000000000357.pica (DE-627)ELV030579856 (ELSEVIER)S0925-2312(17)31173-6 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Li, Weifeng verfasserin aut Weighted contourlet binary patterns and image-based fisher linear discriminant for face recognition 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose a novel face representation model, called the weighted Contourlet binary patterns (WCBP), based on the NonSubsampled Contourlet Transform (NSCT), for face recognition. The decomposition using NSCT can capture rich image information at multiple scales, orientations, and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each NSCT-decomposed image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image (named as WCBP+FLD) for dimension reduction of features. Our extensive experiments on the public FERET, CAS-PEAL-R1 and LFW databases demonstrate that the non-weighted Contourlet binary patterns performs better than local Gabor binary patterns. WCBP further improves the recognition rates. WCBP+FLD can achieve much competitive or even better recognition performance compared with the state-of-the-art Gabor feature based face recognition methods. We propose a novel face representation model, called the weighted Contourlet binary patterns (WCBP), based on the NonSubsampled Contourlet Transform (NSCT), for face recognition. The decomposition using NSCT can capture rich image information at multiple scales, orientations, and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each NSCT-decomposed image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image (named as WCBP+FLD) for dimension reduction of features. Our extensive experiments on the public FERET, CAS-PEAL-R1 and LFW databases demonstrate that the non-weighted Contourlet binary patterns performs better than local Gabor binary patterns. WCBP further improves the recognition rates. WCBP+FLD can achieve much competitive or even better recognition performance compared with the state-of-the-art Gabor feature based face recognition methods. Wang, Yichuan oth Xu, Zhen oth Jiang, Yinyan oth Lu, Zongqing oth Liao, Qingmin oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:267 year:2017 day:6 month:12 pages:436-446 extent:11 https://doi.org/10.1016/j.neucom.2017.06.045 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 267 2017 6 1206 436-446 11 |
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10.1016/j.neucom.2017.06.045 doi GBV00000000000357.pica (DE-627)ELV030579856 (ELSEVIER)S0925-2312(17)31173-6 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Li, Weifeng verfasserin aut Weighted contourlet binary patterns and image-based fisher linear discriminant for face recognition 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose a novel face representation model, called the weighted Contourlet binary patterns (WCBP), based on the NonSubsampled Contourlet Transform (NSCT), for face recognition. The decomposition using NSCT can capture rich image information at multiple scales, orientations, and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each NSCT-decomposed image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image (named as WCBP+FLD) for dimension reduction of features. Our extensive experiments on the public FERET, CAS-PEAL-R1 and LFW databases demonstrate that the non-weighted Contourlet binary patterns performs better than local Gabor binary patterns. WCBP further improves the recognition rates. WCBP+FLD can achieve much competitive or even better recognition performance compared with the state-of-the-art Gabor feature based face recognition methods. We propose a novel face representation model, called the weighted Contourlet binary patterns (WCBP), based on the NonSubsampled Contourlet Transform (NSCT), for face recognition. The decomposition using NSCT can capture rich image information at multiple scales, orientations, and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each NSCT-decomposed image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image (named as WCBP+FLD) for dimension reduction of features. Our extensive experiments on the public FERET, CAS-PEAL-R1 and LFW databases demonstrate that the non-weighted Contourlet binary patterns performs better than local Gabor binary patterns. WCBP further improves the recognition rates. WCBP+FLD can achieve much competitive or even better recognition performance compared with the state-of-the-art Gabor feature based face recognition methods. Wang, Yichuan oth Xu, Zhen oth Jiang, Yinyan oth Lu, Zongqing oth Liao, Qingmin oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:267 year:2017 day:6 month:12 pages:436-446 extent:11 https://doi.org/10.1016/j.neucom.2017.06.045 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 267 2017 6 1206 436-446 11 |
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10.1016/j.neucom.2017.06.045 doi GBV00000000000357.pica (DE-627)ELV030579856 (ELSEVIER)S0925-2312(17)31173-6 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Li, Weifeng verfasserin aut Weighted contourlet binary patterns and image-based fisher linear discriminant for face recognition 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose a novel face representation model, called the weighted Contourlet binary patterns (WCBP), based on the NonSubsampled Contourlet Transform (NSCT), for face recognition. The decomposition using NSCT can capture rich image information at multiple scales, orientations, and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each NSCT-decomposed image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image (named as WCBP+FLD) for dimension reduction of features. Our extensive experiments on the public FERET, CAS-PEAL-R1 and LFW databases demonstrate that the non-weighted Contourlet binary patterns performs better than local Gabor binary patterns. WCBP further improves the recognition rates. WCBP+FLD can achieve much competitive or even better recognition performance compared with the state-of-the-art Gabor feature based face recognition methods. We propose a novel face representation model, called the weighted Contourlet binary patterns (WCBP), based on the NonSubsampled Contourlet Transform (NSCT), for face recognition. The decomposition using NSCT can capture rich image information at multiple scales, orientations, and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each NSCT-decomposed image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image (named as WCBP+FLD) for dimension reduction of features. Our extensive experiments on the public FERET, CAS-PEAL-R1 and LFW databases demonstrate that the non-weighted Contourlet binary patterns performs better than local Gabor binary patterns. WCBP further improves the recognition rates. WCBP+FLD can achieve much competitive or even better recognition performance compared with the state-of-the-art Gabor feature based face recognition methods. Wang, Yichuan oth Xu, Zhen oth Jiang, Yinyan oth Lu, Zongqing oth Liao, Qingmin oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:267 year:2017 day:6 month:12 pages:436-446 extent:11 https://doi.org/10.1016/j.neucom.2017.06.045 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 267 2017 6 1206 436-446 11 |
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10.1016/j.neucom.2017.06.045 doi GBV00000000000357.pica (DE-627)ELV030579856 (ELSEVIER)S0925-2312(17)31173-6 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Li, Weifeng verfasserin aut Weighted contourlet binary patterns and image-based fisher linear discriminant for face recognition 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose a novel face representation model, called the weighted Contourlet binary patterns (WCBP), based on the NonSubsampled Contourlet Transform (NSCT), for face recognition. The decomposition using NSCT can capture rich image information at multiple scales, orientations, and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each NSCT-decomposed image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image (named as WCBP+FLD) for dimension reduction of features. Our extensive experiments on the public FERET, CAS-PEAL-R1 and LFW databases demonstrate that the non-weighted Contourlet binary patterns performs better than local Gabor binary patterns. WCBP further improves the recognition rates. WCBP+FLD can achieve much competitive or even better recognition performance compared with the state-of-the-art Gabor feature based face recognition methods. We propose a novel face representation model, called the weighted Contourlet binary patterns (WCBP), based on the NonSubsampled Contourlet Transform (NSCT), for face recognition. The decomposition using NSCT can capture rich image information at multiple scales, orientations, and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each NSCT-decomposed image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image (named as WCBP+FLD) for dimension reduction of features. Our extensive experiments on the public FERET, CAS-PEAL-R1 and LFW databases demonstrate that the non-weighted Contourlet binary patterns performs better than local Gabor binary patterns. WCBP further improves the recognition rates. WCBP+FLD can achieve much competitive or even better recognition performance compared with the state-of-the-art Gabor feature based face recognition methods. Wang, Yichuan oth Xu, Zhen oth Jiang, Yinyan oth Lu, Zongqing oth Liao, Qingmin oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:267 year:2017 day:6 month:12 pages:436-446 extent:11 https://doi.org/10.1016/j.neucom.2017.06.045 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 267 2017 6 1206 436-446 11 |
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Enthalten in The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast Amsterdam volume:267 year:2017 day:6 month:12 pages:436-446 extent:11 |
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The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
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Li, Weifeng @@aut@@ Wang, Yichuan @@oth@@ Xu, Zhen @@oth@@ Jiang, Yinyan @@oth@@ Lu, Zongqing @@oth@@ Liao, Qingmin @@oth@@ |
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weighted contourlet binary patterns and image-based fisher linear discriminant for face recognition |
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Weighted contourlet binary patterns and image-based fisher linear discriminant for face recognition |
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We propose a novel face representation model, called the weighted Contourlet binary patterns (WCBP), based on the NonSubsampled Contourlet Transform (NSCT), for face recognition. The decomposition using NSCT can capture rich image information at multiple scales, orientations, and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each NSCT-decomposed image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image (named as WCBP+FLD) for dimension reduction of features. Our extensive experiments on the public FERET, CAS-PEAL-R1 and LFW databases demonstrate that the non-weighted Contourlet binary patterns performs better than local Gabor binary patterns. WCBP further improves the recognition rates. WCBP+FLD can achieve much competitive or even better recognition performance compared with the state-of-the-art Gabor feature based face recognition methods. |
| abstractGer |
We propose a novel face representation model, called the weighted Contourlet binary patterns (WCBP), based on the NonSubsampled Contourlet Transform (NSCT), for face recognition. The decomposition using NSCT can capture rich image information at multiple scales, orientations, and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each NSCT-decomposed image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image (named as WCBP+FLD) for dimension reduction of features. Our extensive experiments on the public FERET, CAS-PEAL-R1 and LFW databases demonstrate that the non-weighted Contourlet binary patterns performs better than local Gabor binary patterns. WCBP further improves the recognition rates. WCBP+FLD can achieve much competitive or even better recognition performance compared with the state-of-the-art Gabor feature based face recognition methods. |
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We propose a novel face representation model, called the weighted Contourlet binary patterns (WCBP), based on the NonSubsampled Contourlet Transform (NSCT), for face recognition. The decomposition using NSCT can capture rich image information at multiple scales, orientations, and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each NSCT-decomposed image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image (named as WCBP+FLD) for dimension reduction of features. Our extensive experiments on the public FERET, CAS-PEAL-R1 and LFW databases demonstrate that the non-weighted Contourlet binary patterns performs better than local Gabor binary patterns. WCBP further improves the recognition rates. WCBP+FLD can achieve much competitive or even better recognition performance compared with the state-of-the-art Gabor feature based face recognition methods. |
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Wang, Yichuan Xu, Zhen Jiang, Yinyan Lu, Zongqing Liao, Qingmin |
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