Real-time pose invariant spontaneous smile detection using conditional random regression forests
Real-time pose invariant spontaneous smile detection using conditional random regression forests
复制标题
使用条件随机回归森林进行实时姿势不变自发微笑检测
DOI：
10.1016/j.ijleo.2019.01.020
复制
发表时间：
2019-04-01
期刊：
影响因子：
3.1
通讯作者：
Chen, Jingying
中科院分区：
文献类型：
--
作者：
Liu, Leyuan;Gui, Wenting;Chen, Jingying
在无约束环境中检测自然微笑是一个具有挑战性的问题，这主要是由于头部姿态导致的类内差异较大。本文提出了一种基于条件随机回归森林的实时微笑检测方法。由于图像块与微笑强度之间的关系是基于头部姿态进行建模的，所以所提出的微笑检测方法对头部姿态不敏感。为了实现较高的微笑检测性能，采用了包括回归森林、多标签数据集扩充和无信息块去除等技术。实验结果表明，尽管使用的是手工特征，但所提出的方法在两个具有挑战性的真实数据集上取得了与基于最先进的深度神经网络的方法相当的性能。还提出了一种动态森林集成方案，以在微笑检测性能和处理速度之间进行权衡。与深度神经网络不同，所提出的方法可以在没有GPU的通用硬件上实时运行。
Detecting spontaneous smile in unconstrained environment is a challenging problem mainly due to the large intra-class variations caused by head poses. This paper presents a real-time smile detection method based on conditional random regression forests. Since the relation between image patches and smile intensity is modelled conditional to head pose, the proposed smile detection method is not sensitive to head poses. To achieve high smile detection performance, techniques including regression forest, multiple-label dataset augmentation and non-informative patch removement are employed. Experimental results show that the proposed method achieves competitive performance to state-of-the-art deep neural network based methods on two challenging real-world datasets, although using hand-crafted features. A dynamical forest ensemble scheme is also presented to make a trade-off between smile detection performance and processing speed. In contrast to deep neural networks, the proposed method can run in real-time on general hardware without GPU.