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收稿：2019-06-07，
修回：2019-12-25，
录用：2020-1-1，
纸质出版：2020-08-16
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目的
2
针对基于Haar-like特征的Adaboost人脸检测算法，在应用于视频流时训练的时间较长，以及检测效率较低的问题，提出了一种基于区间阈值的Adaboost人脸检测算法。
方法
2
通过运行传统的Adaboost算法对人脸图像Haar-like特征值进行提取分析后，对人脸样本与非人脸样本特征值进行比较，发现在某一特定的特征值区间内，人脸和非人脸区域能够得到准确区分，根据此特性，进行分类器的选择，在简化弱分类器计算步骤的同时，降低训练时间，提高对人脸的识别能力。除此之外，弱分类器的增强通过Adaboost算法的放大使得强分类器分类精度提高，与级联结构的配合使用也提升了最终模型检测人脸的准确率。
结果
2
利用MIT（Massachusetts Institute of Technology）标准人脸库对改进Adaboost算法的性能进行验证，通过实验验证结果可知，改进后的Adaboost人脸检测算法训练速度提升为原来的1.44倍，检测率上升到94.93%，虚警率下降到6.03%。并且将改进算法在ORL（Olivetti Research Laboratory）、FERET（face recognition technology）以及CMU Multi-PIE（the CMU Multi-PIE face database）这3种标准人脸库中，分别与SVM（support vector machine）、DL（deep learning）、CNN（convolutional neural networks）以及肤色模型等4种算法进行了人脸检测对比实验，实验结果显示，改进后的Adaboost算法在进行人脸检测时，检测率提升了2.66%，训练所需时间减少至624.45 s，检测效果明显提升。
结论
2
提出的基于区间阈值的Adaboost人脸检测算法，在分类器的训练和人脸检测方面都比传统的Adaboost算法性能更高，能够更好地满足人员较密集处（如球场等地）对多人脸同时检测的实际需求。
Objective
2
With the continuous improvement of artificial intelligence technology
Adaboost algorithm based on Haar-like features has gained an important position in the endless stream of machine learning algorithms. Adaboost is widely used in the fields of medicine
transportation
and security. However
the Adaboost face detection algorithm based on Haar-like features
which is applied to video streams
has a long training time and low detection efficiency. This paper then proposes a novel Adaboost face detection algorithm based on the interval threshold.
Method
2
The integral value is used to quickly calculate the feature value of a face image
whereas the extracted feature values are used to analyze the Haar-like features of the final face recognition result. Comparing the feature values of face smaples and non-human face samples
and realized that the human and non-human faces are then determined by calculating the frequencies of different feature value intervals
and the final image is drawn. After a statistical analysis of the Haar-like eigenvalues of images
these two types of faces have been well distinguished within a certain eigenvalue interval
and a weak classifier based on double threshold is proposed accordingly. The interval threshold is used to select the weak classifier to simplify the calculation steps
shorten the training time
improve the face recognition and classification ability
and reduce the false alarm rate. Enhancing the weak classifier also improves the classification accuracy of the strong classifier by amplifying the Adaboost algorithm
whereas using a cascade structure increases the final face detection accuracy. This approach greatly accelerates the threshold search
and using the interval threshold instead of a single threshold guarantees an accurate threshold search. An interval threshold weak classifier corresponds to two single threshold weak classifiers
and using a strong classifier can effectively increased detection effect by 2.66%
Besides
training time reduced to 624.45 s.
Result
2
The performance of improved algorithm was compared with that of the traditional Adaboost algorithm and is verified by using the MIT(massachusetts Institute of Technology) standard face database. In the experiment
1 500 and 3 000 face and non-face samples were randomly selected for testing. The experimental simulation results of the traditional and proposed Adaboost algorithms were then compared in terms of training time
detection time
detection rate
and false alarm rate. The training time of the improved algrithm was 1.44 times faster than traditional ones. And the detection rate was improved to 94.93%. Both algorithms showed a low detection rate
low false alarm rate
and poor recognition ability with a small number of weak classifiers. However
increasing the number of weak classifiers improved both detection and false alarm rates. In the case of the same detection rate
the improved Adaboost algorithm demonstrated less requirements for weak classifiers
shorter detection time
and 6.03% lower false alarm rate compared with its traditional counterpart. To verify its high practicability
the improved Adaboost algorithm was applied to real face detection
and the experimental results show that this algorithm outperforms the traditional algorithm in terms of detection efficiency
error rate
and detection accuracy. To verify whether the improved Adaboost algorithm is highly advanced
400
1 400
and 2 592 face images were selected from the ORL(Olivetti Research Laboratory)
FERET(face recognition technology)
and CMU Multi-PIE databases
respectively. The performance of the improved Adaboost algorithm was separately tested on these three groups of faces with the popular SVM(support vector machine)
DL(deep learning)
CNN(convolutional neural networks)
and skin color models. The improved algorithm shows a higher detection efficiency compared with the traditional algorithm and has a 95.3% correct detection rate.
Conclusion
2
This paper proposes a dual-threshold Adaboost fast training algorithm that demonstrates efficient face detection
fast training speed
and excellent test results. Experimental results show that under the same cascade structure and number of weak classifiers
the improved Adaboost algorithm is generally superior to the single threshold. The improved algorithm also has shorter face training time requirements
improved face detection accuracy
and better performance and practicability compared with its traditional counterpart. The ROC(receiver operating characteristic) curves show that the improved algorithm achieves better results with fewer weak classifiers. In actual picture detection applications
the improved Adaboost algorithm is superior to the traditional algorithm in terms of training and detection and can meet actual needs. Therefore
it can better meet the actual demand for simultaneous detection of multiple faces in densely populated areas
such as stadiums.
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