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

Ying Nong Chen, Chin Chuan Han, Gang Feng Ho, Kuo Chin Fan

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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 coarse-level 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.

Original languageEnglish
Article number61477
JournalInternational Journal of Advanced Robotic Systems
Issue number12
StatePublished - 18 Dec 2015


  • Coarse-to-fine Strategy
  • Convolution/Subsampling Feature Map
  • Face Detection
  • Feature Ranking


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