Early-stage Fatigue Detection Based on Frequency Domain Information of Eye Features
The fatigue of baggage X-ray security inspector is an important cause of false and missed inspection.Previous work in this field mostly focused on detecting extreme fatigue with explicit signs such as yawning,nodding off and prolonged eye closure.However,for security inspectors,such explicit signs may not appear until only before an accident,and it is too late to detect fa-tigue.Thus,there is significant value in detecting fatigue at an early stage,to warn the occurrence of fatigue in time.Due to the subtle facial performance characteristics of early-stage fatigue,the irreversibility of time-domain parameters leads to its inability for complete representations.To solve this problem,an early-stage fatigue detection method for baggage X-ray security inspectors based on the frequency domain information of eye features is proposed,which converts the original time domain information into a more expressive frequency domain feature space.It firstly obtained the eye aspect ratio series through the facial detection algo-rithm,then the time-domain features are transformed into frequency-domain space for analysis to mine more subtle features.Fi-nally,HM-LSTM is used for training and verification.Experiment is conducted on the dataset UTA-RLDD.The results show that the proposed architecture improves the recognition rate of early-stage fatigue by 2％,demonstrating that frequency domain features have better expression ability than time domain features.
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