[论文解读] Developing a gender classification approach in human face images using modified local binary patterns and tani-moto based nearest neighbor algorithm

Shervan Fekri-Ershad|arXiv (Cornell University)|Jan 29, 2020

Face and Expression Recognition参考文献 32被引用 8

一句话总结

本文提出一种基于改进局部二值模式（MLBP）进行特征提取，并结合基于Tani-Moto距离的最近邻分类器的人脸图像性别分类系统。该方法在旋转和光照变化下表现出高准确率、强鲁棒性以及更低的计算复杂度，适用于对效率和内存占用要求较低的实时智能手机应用。

ABSTRACT

Human identification is a much attention problem in computer vision. Gender classification plays an important role in human identification as preprocess step. So far, various methods have been proposed to solve this problem. Absolutely, classification accuracy is the main challenge for researchers in gender classification. But, some challenges such as rotation, gray scale variations, pose, illumination changes may be occurred in smart phone image capturing. In this respect, a multi step approach is proposed in this paper to classify genders in human face images based on improved local binary patters (MLBP). LBP is a texture descriptor, which extract local contrast and local spatial structure information. Some issues such as noise sensitivity, rotation sensitivity and low discriminative features can be considered as disadvantages of the basic LBP. MLBP handle disadvantages using a new theory to categorize extracted binary patterns of basic LBP. The proposed approach includes two stages. First of all, a feature vector is extracted for human face images based on MLBP. Next, non linear classifiers can be used to classify gender. In this paper nearest neighborhood classifier is evaluated based on Tani-Moto metric as distance measure. In the result part, two databases, self-collected and ICPR are used as human face database. Results are compared by some state-ofthe-art algorithms in this literature that shows the high quality of the proposed approach in terms of accuracy rate. Some of other main advantages of the proposed approach are rotation invariant, low noise sensitivity, size invariant and low computational complexity. The proposed approach decreases the computational complexity of smartphone applications because of reducing the number of database comparisons. It can also improve performance of the synchronous applications in the smarphones because of memory and CPU usage reduction.

研究动机与目标

• 解决在光照变化、姿态变化和噪声等真实条件下人脸图像中准确性别分类的挑战。
• 克服传统局部二值模式（LBP）存在的噪声敏感性、旋转依赖性以及判别能力弱等局限。
• 开发一种计算效率高且鲁棒的性别分类框架，适用于资源受限设备（如智能手机）的部署。
• 通过在最近邻分类器中集成一种新型距离度量（Tani-Moto），提升性别特征的判别能力，从而改善分类性能。

提出的方法

• 使用改进局部二值模式（MLBP）从人脸图像中提取纹理特征，以增强判别能力，并降低对噪声和旋转的敏感性。
• 采用基于Tani-Moto距离度量的最近邻算法，实施非线性分类策略，以提升分类准确率。
• 采用两阶段流程：首先通过MLBP进行特征提取，随后利用基于Tani-Moto的k-NN分类器完成性别分类。
• 通过减少分类过程中数据库比对次数，优化系统以实现更低的计算复杂度。
• 使用自建人脸数据库和ICPR数据库进行训练与评估，以确保在多样化数据上的鲁棒性。
• 在k-NN分类器中实现Tani-Moto度量作为距离度量，该度量在处理二值模式方面表现优异，可有效提升分类性能。

实验结果

研究问题

• RQ1 MLBP能否在不同光照和姿态条件下，有效提取人脸图像中鲁棒且具有判别性的纹理特征？
• RQ2 与传统距离度量相比，Tani-Moto距离度量在处理二值特征向量时，能否显著提升性别分类的准确率？
• RQ3 与现有性别分类方法相比，所提出方法在计算复杂度方面降低了多少？
• RQ4 与标准LBP及其他先进方法相比，该系统在旋转不变性和抗噪性方面表现如何？
• RQ5 由于内存和CPU使用率降低，该框架是否可高效部署于移动设备？

主要发现

• 所提方法在自建数据库和ICPR数据库上均表现出较高的性别分类准确率，优于多种先进算法。
• 由于MLBP和Tani-Moto度量固有的不变性特性，系统对旋转、光照变化和噪声表现出强鲁棒性。
• 计算复杂度显著降低，通过减少数据库比对次数，实现了在智能手机上的高效部署。
• 该方法具有尺寸不变性，在小尺寸或尺寸变化的人脸图像上仍能保持高性能。
• 与使用欧氏距离或汉明距离的标准度量相比，基于Tani-Moto的最近邻分类器在二值特征向量上显著提升了分类准确率。
• 该框架显著降低了内存和CPU使用率，适用于移动平台上的实时与同步应用。