{
  "paper_id": "c87cfa3ba24cd95b9f7b028d18445fa8",
  "paper_title": "Person Recognition Based on Face and Body Information for Domestic Service Robots",
  "paper_authors": [
    "Shaghayegh Gharghabi",
    "Reza Safabakhsh"
  ],
  "publication_date": "",
  "publication_venue": "2015 3rd RSI International Conference on Robotics and Mechatronics (ICROM)",
  "abstract": "Non-intrusive person recognition in different poses is a crucial task in the field of human-robot interaction (HRI). Most of the previous work for person recognition were based on face. In these approaches, if the person's face is invisible to the robot, it will ask the person to stand in a location suitable for the robot and look at the robot to recognize him. However in a normal interaction, the robot should be able to recognize a person without user cooperation to have acceptable interaction with users in real-time. In this paper, we address this problem using a combination of face and 3D information of a person's body. We used a mathematical framework based on the Bayesian decision theory for decision making. This combination can be used for recognizing scenarios where the pose of the people is unconstrained which makes it difficult to recognize a person by applying common face recognition algorithms. We tested our proposed approach in recognizing a five person group in various poses such as sitting and circular walking. The method was applied to a service robot equipped with the Kinect sensor. The results show a mean accuracy of 40.48% over all the poses using face alone, an accuracy of 67.23% using body alone, and an accuracy of 80.23% using the combination of the face and body information.",
  "keywords": [
    "Person Recognition",
    "Service robot",
    "Human Robot Interaction",
    "Kinect",
    "Face recognition"
  ],
  "paper_institutions": [
    "Robotic Research Center, Amirkabir University of Technology (Tehran Polytechnic)",
    "Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic)"
  ],
  "output_language": "zh",
  "provider": "doubao",
  "model": "ark-code-latest",
  "prompt_template": "simple",
  "extracted_at": "2026-08-16T19:46:54.290947Z",
  "template_tag": "simple",
  "summary": "### 摘要\n\n本文针对家庭服务机器人在非约束姿态下进行人物识别的问题，提出了一种结合人脸与人体三维信息的概率识别方法。现有方法多依赖人脸，当人脸不可见时需用户配合，影响自然交互。作者基于贝叶斯决策理论，将人脸识别（采用Viola-Jones检测和稀疏表示）与人体骨骼特征（从Kinect提取14个特征）进行概率融合，并引入加权机制以突出人脸特征的判别力。实验在5人（3男2女）的多姿态（坐、圆形行走、直线行走、静止）数据上进行，机器人为装备Kinect的Sepanta。结果表明，单独人脸、单独人体和融合方法的平均准确率分别为40.48%、67.23%和80.23%，融合显著提升性能。该方法可在部分特征缺失时自动退化为可用模态。未来将研究部分遮挡和更多姿态，并自适应选择特征与权重。核心机制为贝叶斯融合公式：P(c_i|x,V) = P(V|c_i)P(c_i|x)/∑P(V|c_i)P(c_i|x)，其中P(v_j|c_i)采用高斯分布估计。\n\n### 关键要点\n\n- 现有基于人脸的人物识别在面部不可见时需用户配合，难以实现非侵入式实时交互。\n- 论文利用Kinect获取的3D骨骼数据作为软生物特征，与高判别力的人脸特征互补。\n- 人脸检测采用Viola-Jones方法，识别采用稀疏表示（l1范数最小化）并基于最小重构误差分类。\n- 人体特征由14个相邻关节间的链接长度和距离和构成，基于站立静止序列估计均值和方差。\n- 融合采用贝叶斯决策理论，假设人脸特征和身体特征独立，并通过高斯分布建模身体特征的条件概率。\n- 为克服等权加权缺陷，提出加权方案：赋予人脸特征权重b0远大于身体特征权重bj，避免因单一身体特征错误导致正确识别被拒绝。\n- 系统在部分模态缺失时（如人脸不可见）自动退化为可用模态进行识别。\n- 实验在5人（3男2女）群体上进行，包含坐、圆形行走、直线行走和静止四种姿态，其中静止用于训练，其余用于测试。\n- 单独人脸、单独身体和融合的平均准确率分别为40.48%、67.23%和80.23%，融合在坐姿下提升最显著（从76.84%提升至81.69%）。\n- 在直线行走中，融合准确率最高达86.69%；圆形行走中，人脸仅部分可见，融合仍达72.33%。\n- 特征权重需针对家庭各成员自适应调整，通过将训练数据分为两部分分别确定各特征权重。\n- 该方法已封装为ROS节点，适用于任何装备Kinect的机器人，并可在家庭环境实时运行。"
}