Weighted Sub-Gabor for face recognition

Nanni, Loris; Maio, Dario

doi:10.1016/j.patrec.2006.09.002

Cited by 51 publications

(16 citation statements)

References 20 publications

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“…The algorithms presented in this case in [22,23] use the Gabor filters bank for extracting facial features in order to discriminate between faces of different people. The proposed algorithm, when with him, uses the filters bank to discriminate face objects of those non-face ones in the image.…”

Section: Gabor Filtersmentioning

confidence: 99%

Face Detection Algorithm based on Skin Detection, Watershed Method and Gabor Filters

Hajraoui¹,

Sabri²

2014

IJCA

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Automatic face detection has been intensively studied for human-related recognition systems. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. In this paper, a new face detection algorithm is proposed. This speedy and robust solution developed, on the one hand is based on the segmentation of the color image to skin regions using a new approach to detect the pixels of the skin and the water shed segmentation method. On the other hand, using Gabor filters, combined with a proposed model of face, skin regions are classified into two classes: face and non-face. The integration of these tools in our algorithm permits to develop a face detector with very reasonable and efficient performances. Experimental results show that the method mentioned in this paper can achieve high detection rates and low false positives. To evaluate the detection speed of proposed algorithm, a comparison with a recent known algorithm is made too.

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Section: Gabor Filtersmentioning

confidence: 99%

Face Detection Algorithm based on Skin Detection, Watershed Method and Gabor Filters

Hajraoui¹,

Sabri²

2014

IJCA

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“…They used a set of automatically selected facial features and computed the normalized cross-correlation between the region of the facial features in the probe and the same region in all gallery images. Nanni and Maio [2] proposed a Weighted Sub-Gabor method in which the Gabor wavelet was applied at each sub-pattern and the extracted Gabor features were projected to a low dimensional space. Ahonen et al [3] applied Local Binary Patterns (LBP) to the face recognition task.…”

Section: Introductionmentioning

confidence: 99%

3D face recognition using topographic high-order derivatives

Cheraghian

Hajati

Mian

et al. 2013

2013 IEEE International Conference on Image Processing

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This paper presents a novel feature, Topographic High-order Derivatives (THD) for 3D face recognition. THD is based on the high-order micro-pattern information extracted from face topography maps. Face topography maps are partitioned into polar sectors, and THDs are computed using directional highorder derivatives within the sectors. Local features are extracted by encoding directional high-order derivatives within polar neighborhoods. To evaluate the proposed method, we use Bosphorus and FRGC 3D face databases which include pose and expression changes. The performance of the proposed method is higher compared to the state-of-the-art benchmark approaches in 3D face recognition.

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“…The dimension of the feature vectors extracted at the sub-sampled points is further reduced by using PCA and then LDA is applied for final face recognition. Nanni and Maio [12] proposed a Weighted Sub-Gabor method which is a local-based approach. In this method, the Gabor wavelet is applied at fixed positions, in correspondence of the nodes of a square-meshed grid superimposed to the face image.…”

Section: Introductionmentioning

confidence: 99%

“…Instead of using holistic representation of face images which is not effective under different facial expressions and partial occlusions, the proposed algorithm utilizes a local Gabor array to represent faces partitioned into sub-patterns. The Sub-Gabor Array representation approach is motivated by the recent research interest drawn on local patterns in face recognition [12][13][14][20][21][22][23][24][25][26][27], which appear to be more robust against variations in facial expression, lighting condition, and pose. Especially, in order to perform matching in the sense of the richness of identity information rather than the size of a local area and to handle the partial occlusion problem, the proposed method employs an adaptively weighting technique to weight the projected Sub-Gabor features into the PCA subspace which is extracted from local areas, based on the importance of the information they contain and their similarities to the corresponding local areas in the general face image.…”

Section: Introductionmentioning

confidence: 99%