In-pit coal mine personnel uniqueness detection technology based on personnel positioning and face recognition

Since the coal mine in-pit personnel positioning system neither can effectively achieve the function to detect the uniqueness of in-pit coal-mine personnel nor can identify and eliminate violations in attendance management such as multiple cards for one person, and swiping one's cards by others in China at present. Therefore, the research introduces a uniqueness detection system and method for in-pit coal-mine personnel integrated into the in-pit coal mine personnel positioning system, establishing a system mode based on face recognition + recognition of personnel positioning card + release by automatic detection. Aiming at the facts that the in-pit personnel are wearing helmets and faces are prone to be stained during the face recognition, the study proposes the ideas that pre-process face images using the 2D-wavelet-transformation-based Mallat algorithm and extracts three face features: miner light, eyes and mouths, using the generalized symmetry transformation-based algorithm. This research carried out test with 40 clean face images with no helmets and 40 lightly-stained face images, and then compared with results with the one using the face feature extraction method based on grey-scale transformation and edge detection. The results show that the method described in the paper can detect accurately face features in the above-mentioned two cases, and the accuracy to detect face features is 97.5% in the case of wearing helmets and lightly-stained faces.

Application of wavelet transform in feature extraction and pattern recognition of wideband echoes

A novel approach to extract edge features from wideband echo is proposed. The set of extracted features not only represents the echo waveform in a concise way, but also is sufficient and well suited for classification of non-stationary echo data from objects with different property.The feature extraction is derived from the Discrete Dyadic Wavlet Transform (DDWT) of the echo through the undecimated algorithm. The motivation we use the DDWT is that it is time-shift-invariant which is beneficial for localization of edge, and the wavelet coefficients at larger scale represent the main shape feature of echo, i.e. edge, and the noise and modulated high-frequency components are reduced with scale increased. Some experimental results using real data which contain 144 samples from 4 classes of lake bottoms with different sediments are provided. The results show that our approach is a prospective way to represent wideband echo for reliable recognition of nonstationary echo with great variability.

Defect Recognition in Thermosonic Imaging

This work is aimed at developing an effective method for defect recognition in thermosonic imaging.The heat mechanism of thermosonic imaging is introduced,and the problem for defect recognition is discussed.For this purpose,defect existing in the inner wall of a metal pipeline specimen and defects embedded in a carbon fiber reinforced plastic（CFRP） laminate are tested.The experimental data are processed by pulse phase thermography（PPT） method to show the phase images at different frequencies,and the characteristic of phase angle vs frequency curve of thermal anomalies and sound area is analyzed.A binary image,which is based on the characteristic value of defects,is obtained by a new recognition algorithm to show the defects.Results demonstrate good defect recognition performance for thermosonic imaging,and the reliability of this technique can be improved by the method.