Abstract：
BACKGROUND: The grooming process involves sexually explicit images or videos sent by the offender to the minor. Although offenders may try to conceal their identity, these sexts often include hand, knuckle, and nail bed imagery.
OBJECTIVE: We present a novel biometric hand verification tool designed to identify online child sexual exploitation offenders from images or videos based on biometric/forensic features extracted from hand regions. The system can match and authenticate hand component imagery against a constrained custody suite reference of a known subject by employing advanced image processing and machine learning techniques.
METHODS: We conducted experiments on two hand datasets: Purdue University and Hong Kong. In particular, the Purdue dataset collected for this study allowed us to evaluate the system performance on various parameters, with specific emphasis on camera distance and orientation.
METHODS: To explore the performance and reliability of the biometric verification models, we considered several parameters, including hand orientation, distance from the camera, single or multiple fingers, architecture of the models, and performance loss functions.
RESULTS: Results showed the best performance for pictures sampled from the same database and with the same image capture conditions.
CONCLUSIONS: The authors conclude the biometric hand verification tool offers a robust solution that will operationally impact law enforcement by allowing agencies to investigate and identify online child sexual exploitation offenders more effectively. We highlight the strength of the system and the current limitations.
OBJECTIVE: We present a novel biometric hand verification tool designed to identify online child sexual exploitation offenders from images or videos based on biometric/forensic features extracted from hand regions. The system can match and authenticate hand component imagery against a constrained custody suite reference of a known subject by employing advanced image processing and machine learning techniques.
METHODS: We conducted experiments on two hand datasets: Purdue University and Hong Kong. In particular, the Purdue dataset collected for this study allowed us to evaluate the system performance on various parameters, with specific emphasis on camera distance and orientation.
METHODS: To explore the performance and reliability of the biometric verification models, we considered several parameters, including hand orientation, distance from the camera, single or multiple fingers, architecture of the models, and performance loss functions.
RESULTS: Results showed the best performance for pictures sampled from the same database and with the same image capture conditions.
CONCLUSIONS: The authors conclude the biometric hand verification tool offers a robust solution that will operationally impact law enforcement by allowing agencies to investigate and identify online child sexual exploitation offenders more effectively. We highlight the strength of the system and the current limitations.
摘要：
背景：修饰过程涉及犯罪者向未成年人发送的色情图像或视频。尽管罪犯可能会试图隐瞒自己的身份，这些性别通常包括手，转向节,和指甲床图像。
目的：我们提出了一种新颖的生物特征手验证工具，旨在根据从手区域提取的生物特征/法医特征，从图像或视频中识别在线儿童性剥削罪犯。通过采用先进的图像处理和机器学习技术,系统可以针对已知主体的受约束监护套件参考来匹配和认证手部分量图像。
方法：我们在普渡大学和香港两个数据集上进行了实验。特别是,为这项研究收集的普渡大学数据集允许我们评估各种参数的系统性能，特别强调相机的距离和方向。
方法：为了探索生物特征验证模型的性能和可靠性，我们考虑了几个参数，包括手部方向，离摄像机的距离,单个或多个手指，模型的体系结构，和性能损失函数。
结果：结果显示，在相同的图像捕获条件下，从相同的数据库采样的图像的性能最佳。
结论：作者得出结论，生物识别手验证工具提供了一个强大的解决方案，通过允许机构更有效地调查和识别在线儿童性剥削罪犯，将在操作上影响执法。我们强调了该系统的优势和当前的局限性。
目的：我们提出了一种新颖的生物特征手验证工具，旨在根据从手区域提取的生物特征/法医特征，从图像或视频中识别在线儿童性剥削罪犯。通过采用先进的图像处理和机器学习技术,系统可以针对已知主体的受约束监护套件参考来匹配和认证手部分量图像。
方法：我们在普渡大学和香港两个数据集上进行了实验。特别是,为这项研究收集的普渡大学数据集允许我们评估各种参数的系统性能，特别强调相机的距离和方向。
方法：为了探索生物特征验证模型的性能和可靠性，我们考虑了几个参数，包括手部方向，离摄像机的距离,单个或多个手指，模型的体系结构，和性能损失函数。
结果：结果显示，在相同的图像捕获条件下，从相同的数据库采样的图像的性能最佳。
结论：作者得出结论，生物识别手验证工具提供了一个强大的解决方案，通过允许机构更有效地调查和识别在线儿童性剥削罪犯，将在操作上影响执法。我们强调了该系统的优势和当前的局限性。
