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(2016) Automated Inference on Criminality using Face Images

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Wu X, Zhang X. Automated inference on criminality using face images[J]. arXiv preprint arXiv:1611.04135, 2016: 4038-4052.

该篇论文利用supervised machine learning(logistic regression, KNN, SVM, CNN) 对criminal (C) 和non-criminal (N) 面部图像进行分类(准确度最高达到89.51%)，并进行一些实验分析C与N群体之间的区别：

  • N群体内部的面部相似度更大，C群体内部的面部差异更大。
  • C和N是两个concentric(同心), distinctive的manifold(流形).
  • The variation of C greater than N.

基于面部特征的人为判断会带有偏见、先决条件等，而CV算法并不会存在这些问题。

1. Data Preparation

  • Dataset包含1856张照片 (1126N+730C, Figure 1). 照片标准: Chinese, male, between ages of 18 and 55, no facial hair, no facial scars, or other markings.
  • N including waiters, construction workers, taxi and truck drivers, real estate agents, doctors, lawyers and professors; half have university degrees.
  • C including the ministry of public security of China, the departments of public security for the provinces of Guangdong, Jiangsu, Liaoning, etc. And the City police department in China.
  • C中 235人是violent crimes (murder, rape, assault, kidnap and robbery), 剩余536人是non-violent crimes (murder, rape, assault, kidnap and robbery).
  • Only the region of the face and upper neck is extracted.
  • 80 × 80 images.
  • 将每张图像的直方图与整个数据集的平均直方图相匹配，从而使得灰度图归一化到同样的强度分布。


2. Methods

  • 面部关键点特征能够避免signal level和variant of source cameras的影响。论文使用以下四种关键点:
    1. Facial landmark point.
    2. Facial feature vector, generated by modular PCA.
    3. Facial feature vector based on Local Binary Pattern (LBP) histograms.
    4. The concatenation of above three feature vectors.

(Feature-driven classifiers (LR, SVM, KNN) 3 + Data-driven classifiers (CNN)) 10-fold cross validation = 130 cases

3. Results











  • 不同的source camera拍摄的照片可能会带有不同camera的signatures, 虽然已通过上述的landmark point解决，但在此进一步引入高斯噪声 (mean=0) 来overpower camera signatures. 实验结果与期望的一致: 性能不会出现很大的变化 (Figure 6,7;Table 2, 3).








4. Discriminating Feature

  • 使用Feature Generating Machine (FGM)进行分析与犯罪最相关的面部部位，得出这些特征位于眼角、嘴唇和额头部位 (Figure 8).
    1. ρ: 上嘴唇的曲度
    2. d: 内眼角之间的距离
    3. θ: 鼻尖到嘴唇两角的角度




  • 使用Hellinger距离分别计算C和N两者之间的上述3个部位的距离，分别为0.3208, 0.2971, 0.3855. 因此，C和N是存在一定差异的.


  • 按照论文分析结果 (Figure 8, Table 4)脑补了一个极端的罪犯例子


  • 三个特征的直方图


5. Face Clustering on Manifold

  • 通过平均脸并不能很好地得出C和N群体的区别 (Figure 10)，因此需要在更高维度 (manifold流形和聚类)上进行分析。


  • 公式2分别为cross-class average manifold和in-class average manifold.


  • 计算得到manifold后，使用Isomap进行降维可视化。