Facial/License Plate Detection Using a Two-level Cascade Classifier and a Single Convolutional Feature Map

Abstract

In this paper, an object detector is proposed based on a convolution/subsampling feature map and a two-level cascade classifier. First, a convolution/subsampling operation alleviates illumination, rotation and noise variances. Then, two classifiers are concatenated to check a large number of windows using a coarse-to-fine strategy. Since the sub-sampled feature map with enhanced pixels was fed into the coarse-level classifier, the checked windows were drastically reduced to a quarter of the original image. A few remaining windows showing detailed data were further checked using a fine-level classifier.In addition to improving the detection process, the proposed mechanism also sped up the training process. Some features generated from the prototypes within the small window were selected and trained to obtain the coarselevel classifier. Moreover, a feature ranking algorithm reduced the large feature pool to a small set, thus speeding up the training process without losing detection performance. The contribution of this paper is twofold: first, the coarse-to-fine scheme shortens both the training and detection processes. Second, the feature ranking algorithm reduces training time. Finally, some experimental results were achieved for evaluation. From the results, the proposed method was shown to outperform the rapidly performing Adaboost, as well as forward feature selection methods.

Automatic Detection and Recognition of Car Plates Based on Cascade Classifier

“…This data is used as input into the AdaBoost algorithm. The AdaBoost algorithm is one of the steps of machine learning to detect an object within an image [11]. The data weighting value converts the rating from weak in other methods to strong in this method.…”

Simultaneous license plate recognition and face detection application at the edge

“…Y.N.Chen et al has two papers related to the combination of face and license plate detection. 25,26 In , 25 two detectors are proposed, one for face and the other for license plates, both based on a modified CNN verifier. Pyramid-based localization techniques were applied to fuse the candidates and to identify the regions of faces or license plates.…”

“…Pyramid-based localization techniques were applied to fuse the candidates and to identify the regions of faces or license plates. In, 26 a facial/license plate detection method using a two-level cascade classifier and a single convolutional feature map is defined; a coarse-to-fine cascade classifier was designed to detect facial or LP objects. These two papers are focus on the detectors and classifiers for face and license plate objects and do not deal with the OCR part.…”