In this paper, two detectors, one for face and the other for license plates, are proposed, both based on a modified convolutional neural network(CNN) verifier. In our proposed verifier, a single feature map and a fully connected MLP were trained by examples to classify the possible candidates. Pyramid-based localization techniques were applied to fuse the candidates and to identify the regions of faces or license plates. In addition, geometrical rules filtered out false alarms in license plate detection. Some experimental results are given to show the effectiveness of the approach. Keywords: Face detection, license plate detection, convolution neural network, feature map.