150 Copyright © 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 7 DOI: 10.4018/978-1-7998-1839-7.ch007 ABSTRACT This chapter presents a study to identify with classifcation techniques and digital recognition through the construction of a prototype phase that predicts criminal behavior detected in video cameras ob- tained from a free platform called MOTChallenge. The qualitative and descriptive approach, which starts from individual attitudes, expresses a person in his expression, anxiety, fear, anger, sadness, and neutrality through data collection and feeding of some algorithms for assisted learning. This prototype begins with a degree higher than 40% on a scale of 1-100 of a person suspected, subjected to a two- and three-iterations training parameterized into four categories—hood, helmet, hat, anxiety, and neutral- ity—where through orange and green boxes it is signaled at the time of the detection and classifcation of a possible suspect, with a stability of the 87.33% and reliability of the 96.25% in storing information for traceability and future use. Digital Detection of Suspicious Behavior With Gesture Recognition and Patterns Using Assisted Learning Algorithms Nancy E. Ochoa Guevara Fundación Universitaria Panamericana, Colombia Andres Esteban Puerto Lara https://orcid.org/0000-0002-3818-5667 Fundación Universitaria Panamericana, Colombia Nelson F. Rosas Jimenez Fundación Universitaria Panamericana, Colombia Wilmar Calderón Torres Fundación Universitaria Panamericana, Colombia Laura M. Grisales García Fundación Universitaria Panamericana, Colombia Ángela M. Sánchez Ramos Fundación Universitaria Panamericana, Colombia Omar R. Moreno Cubides Fundación Universitaria Panamericana, Colombia