Lin-Lin Huang

Robust face detection using Gabor filter features

In this paper, we present a classification-based face detection method using Gabor filter features. Taking advantage of the desirable characteristics of spatial locality and orientation selectivity of Gabor filters, we design four filters corresponding to four orientations for extracting facial features from local images in sliding windows. The feature vector based on Gabor filters is used as the input of the face/non-face classifier, which is a polynomial neural network (PNN) on a reduced feature subspace learned by principal component analysis (PCA). The effectiveness of the proposed method is demonstrated by experiments on a large number of images. We show that using both of the magnitude and phase of Gabor filter response as features, the detection performance is better than that using magnitude only, and using the real part only also performs fairly well. Our detection performance is competitive with those reported in the literature.

Face detection from cluttered images using a polynomial neural network

Abstract Automatic detection of human faces from cluttered images is important for face recognition and security applications. This problem is challenging due to the multitude of variations and the confusion between face and background regions. This paper proposes a new face detection method using a polynomial neural network (PNN). To locate the human faces in an image, the local regions in multiscale sliding windows are classified by the PNN to two classes, namely, face and non-face. The PNN takes as inputs the binomials of the projection of the local image onto a feature subspace learned by principal component analysis (PCA). We investigated the influence of PCA on either the face samples or the pooled face and non-face samples. In addition, we integrate the distance from the feature subspace into the PNN to improve the detection performance. In experiments on images with complex backgrounds, the proposed method has produced promising results in terms of high detection rate and low false positive rate.

Gradient feature extraction for classification-based face detection

Face detection from cluttered images is challenging due to the wide variability of face appearances and the complexity of image backgrounds. This paper proposes a classification-based method for locating frontal faces in cluttered images. To improve the detection performance, we extract gradient direction features from local window images as the input of the underlying two-class classifier. The gradient direction representation provides better discrimination ability than the image intensity, and we show that the combination of gradient directionality and intensity outperforms the gradient feature alone. The underlying classifier is a polynomial neural network (PNN) on a reduced feature subspace learned by principal component analysis (PCA). The incorporation of the residual of subspace projection into the PNN was shown to improve the classification performance. The classifier is trained on samples of face and non-face images to discriminate between the two classes. The superior detection performance of the proposed method is justified in experiments on a large number of images.

Classification-based face detection using Gabor filter features

This work proposes a classification-based face detection method using Gabor filter features. Considering the desirable characteristics of spatial locality and orientation selectivities of the Gabor filter, we design four filters for extracting facial features from the local image. The feature vector based on Gabor filters is used as the input of the classifier, which is a polynomial neural network (PNN) on a reduced feature subspace learned by principal component analysis (PCA). The effectiveness of the proposed method is demonstrated by the experimental results on testing a large number of images and the comparison with the state-of-the-art method.

A multi-expert approach for robust face detection

Both detection accuracy and speed are of major concerns in developing a robust face detection system for real-world applications. To this end, we propose a robust face detection approach by combining multiple experts in both cascade and parallel manner. We design three detection experts which employ different feature representation schemes of local images: 2D Haar wavelet, gradient direction, and Gabor filter. The three features are classified using the same classification model, namely, a polynomial neural network (PNN) on reduced feature subspace. The detection experts are used in multiple stages with simple ones in proceeding stages and complex ones in succeeding stages for improving detection speed. Meanwhile, the output of each expert is combined with the outputs of its preceding experts to improve detection accuracy. The effectiveness of the multi-expert approach has been demonstrated in experiments on a large number of images. The obtained detection results are superior to the best individual expert and state-of-the-art approaches while the detection speed is fast.

Combining classifiers for robust face detection

In this paper, we propose a face detection method by combining classifiers. We apply two classifiers using features extracted from complementary feature subspaces learned by principal component analysis (PCA). The two classifiers employ the same classification model named a polynomial neural network (PNN). The outputs of the two classifiers are fused to make the final decision. The effectiveness of the proposed method has been demonstrated in experimentals.

Face detection using a modified radial basis function neural network

Face detection from cluttered images is very challenging due to the diverse variation of face appearance and the complexity of image background. In this paper, we propose a neural network based approach for locating frontal views of human faces in cluttered images. We use a radial basis function network (RBFN) for separation of face and non-face patterns and the complexity of RBFN is reduced by principal component analysis (PCA). The influence of the number of hidden units and the configuration of basis functions on the detection performance was investigated. To further improve the performance, we integrate the distance from feature subspace into the RBFN. The proposed method has achieved high detection rate and low false positive rate on testing a large number of images.

Palmprint recognition using polynomial neural network

In this paper, we propose a robust palmprint recognition approach Firstly, a salient-point based method is applied to segment as well as align the region of interest (ROI) from the palmprint image Then, a subspace projection technique, namely, independent component analysis (ICA) is performed on the ROI to extract features Finally, a polynomial neural network (PNN) is used to make classification on reduced feature subspace The effectiveness of the proposed method has been demonstrated in experiments.

Classification-based face detection using compound features

In this paper, we propose a classification-based face detection method using compound features. Four kinds of features, namely, intensity, Gabor filter feature, decomposed gradient feature, and Harr wavelet feature are combined to construct a compound feature vector. The projection of the feature vector on a reduced feature subspace learned by principal component analysis (PCA) is used as the input of the underlying classifier, which is a polynomial neural network (PNN). The experimental results on testing a large number of images demonstrate the effectiveness of the proposed method.

Robust face detection from complex scene using multi-experts

In this paper, we propose a robust face detection approach by combining multiple experts in both cascade and parallel manner. We design three detection experts which employ different feature representation schemes of local images: 2D Haar wavelet, gradient direction, and Gabor filter. The three features are classified using the same classification model, namely, a polynomial neural network (PNN) on reduced feature subspace. The detection experts are used in multiple stages. At each stage, only when the output similarity of face exceeds a threshold, is the succeeding expert invoked to output a new similarity. To speed up detection, simpler (less time consuming) experts are used in preceding stages and complex experts are used in the succeeding stages. Meanwhile, the output of each expert is combined with the outputs of its preceding experts to improve detection accuracy. The effectiveness of the multi-expert approach has been demonstrated in experiments on a large number of images.