目的 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 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 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 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 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 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 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 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.