人脸检测及人脸年龄与性别识别方法

发布时间：2018-03-04 10:35

本文选题：人脸检测　切入点：候选区域-快速卷积神经网络　出处：《中国科学技术大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着媒体和社交网络的发展,人脸年龄与性别识别在现实生活中的应用越来越多,吸引了广泛的研究兴趣。由于人脸图像的生物特征识别是非接触的,比较简单快速,还具有一定的娱乐性,在社交网络、视频监控、人机交互等领域具有广阔的应用前景。本文主要研究了人脸检测方法,以及人脸年龄与性别识别方法,并分别提出两种解决方案,以适应不同的应用场景。第一种方案,使用Faster R-CNN算法进行人脸检测,提取人脸的CNN特征进行训练和测试。第二种方案,使用基于比例特征和Adaboost算法进行人脸检测,提取图像的LBP特征作为人脸特征。上述两种方案提取特征之后,均使用随机森林进行训练和测试,具体内容如下:(1)第一种方案,由于Faster R-CNN算法在各个目标检测数据集上取得惊人的成绩,因此本文在WIDER大规模人脸数据集上,训练一个Faster R-CNN模型进行人脸检测,并在FDDB数据库上对该模型进行评估,结果表明该算法有较高的人脸检测率。为了提高在非限制性环境下对人脸年龄与性别的识别准确率,本文提出一种基于深度卷积神经网络的人脸特征提取方法,使用"一般到特殊"的微调方案。首先采用在大规模数据集上进行人脸识别预训练得到的VGG-Face模型;接着使用该模型在CelebA人脸属性数据集上,对选取的5个特定的属性进行微调训练,得到人脸属性模型,这几个属性分别是:①是否留胡子,②是否年轻,③是否戴眼镜,④性别是否为男,⑤是否戴帽子。将所有全连接层的输出值连接起来,构成一个向量,作为人脸特征;最后使用随机森林分类器,在Adience数据集上训练和测试。实验结果表明,该方法的分类准确率较高,提取的人脸CNN特征具有鲁棒性。(2)第二种方案,提出基于比例特征和Adaboost的人脸检测算法,然后提取图像LBP直方图作为人脸特征向量。具体的,本文提出的比例特征,描述的是图像中任意两个点的比例关系,它具有尺度不变性,有界性等特点。本文使用深度二次树去学习比例特征及其组合的最优子集,使得人脸不同部位可以通过学习的规则被分割,再使用一个soft-cascade级联结构的分类器对滑动窗口进行分类,检测人脸位置。接着,本文使用图像分块的方法,分别提取各级人脸图像的LBP直方图特征,并使用随机森林算法进行训练和测试。该方法的实验结果跟上述基于人脸CNN特征的分类方法相比,准确率要低一些。
[Abstract]:With the development of media and social network, face age and gender recognition is more and more widely used in real life, attracting wide research interest. Because the biometric recognition of face image is non-contact, it is relatively simple and fast. It has a wide application prospect in social network, video surveillance, human-computer interaction and so on. This paper mainly studies the face detection method, as well as the face age and gender recognition method. Two solutions are proposed to adapt to different application scenarios. First, face detection using Faster R-CNN algorithm, CNN feature extraction for training and testing. Face detection is based on scale feature and Adaboost algorithm, and LBP feature of image is extracted as face feature. After these two schemes are extracted, they are trained and tested by random forest. The concrete contents are as follows: 1) the first scheme. Because the Faster R-CNN algorithm has achieved remarkable results in each target detection data set, this paper trains a Faster R-CNN model on the WIDER large-scale face data set to detect the face, and evaluates the model on the FDDB database. The results show that the algorithm has high face detection rate. In order to improve the accuracy of face age and gender recognition in the unrestricted environment, this paper proposes a face feature extraction method based on deep convolution neural network. Using the "general-to-special" fine-tuning scheme. Firstly, the VGG-Face model, which is pre-trained on large-scale data sets for face recognition, is used, and then the model is used on the CelebA face attribute data set. The five selected attributes are trained to fine tune, and the face attribute model is obtained. These attributes are: 1, whether he has a beard, whether he is young, whether he is wearing glasses, whether he is a man, and whether he wears a hat. All the output values of the full connection layer are connected together to form a vector as a feature of a face; Finally, a random forest classifier is used to train and test the Adience dataset. The experimental results show that the classification accuracy of this method is high, and the extracted face CNN features are robust. A face detection algorithm based on scale feature and Adaboost is proposed, and then the LBP histogram of the image is extracted as the face feature vector. It has the characteristics of scale invariance and boundedness. In this paper, we use the deep quadratic tree to learn the optimal subset of the scale feature and its combination, so that different parts of the face can be segmented by learning rules. Then a classifier with soft-cascade cascade structure is used to classify the sliding window to detect the position of the face. Then, the LBP histogram features of the face images at all levels are extracted by using the method of image segmentation. The experimental results of this method are lower than the classification method based on face CNN feature.
【学位授予单位】：中国科学技术大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41

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