1. 研究目的与意义（文献综述）

在图像分类[1]、语音识别、自然语音等多个领域深度学习[2]都取得了巨大成功，而目前最热门的人脸识别[3]便是其中最为典型的应用案例。

人脸识别是通过人的面部信息进行身份确认的生物特征识别技术，细数起来已有数十年的研究历史。

一般来说，人脸识别系统由人脸检测、特征点定位、人脸识别等模块组成，其中面部识别主要包括这三种方式：几何结构、子空间局部特征以及深度学习。

2. 研究的基本内容与方案

基本内容及目标
本设计利用多任务卷积神经网络（multi-task convolutional neural networks, mtcnn）在图像与视频中检测人脸，包括：mtcnn模型包含的三个网络p-net,r-net,o-net的功能与结构，mtcnn模型构建与训练，利用导出模型设计人脸检测与人脸对齐算法。

拟采用的方案及措施
本设计系统开发平台是ubuntu系统，卷积神经网络的开发框架为caffe。

在此基础上综合运用所学的c 等编程知识完成系统的设计与实现工作。

3. 研究计划与安排

(1)第1周至第2周：查阅有关的参考资料并完成开题报告；翻译英文资料（不少于5000汉字），并交予指导教师检查。

(2）第3周至第10周：熟悉所选用的开发平台，运用所学的软件设计理论，完成整个系统的前期设计工作。

进行系统的编码、调试、集成、测试工作。

4. 参考文献（12篇以上）

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[2] [美]Ian Goodfellow，[加]Yoshua Bengio著；深度学习，人民邮电出版社，2017-08
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