No abstract

The objective of this work is to present a model of neural network for the detection and segmentation of human's body parts (HPNet). We offer a multiplatform real-time solution that can be run on ordinary computers, as well as on mobile devices and embedded systems. Our proposal is characterized by presenting a compact solution, and by investigating a part of object detection still little explored. One of the striking features presented is the ability to recognize parts of the human body even in uncontrolled environments, due to the use of a random subset of Google's public database (Open Images Dataset) that contains images with objects in the most varied sizes, positions, lighting and occlusion conditions. At first, we offer a solution only for the detection and segmentation of the common parts of the human body, but we intend to expand its capabilities to detect other more specific parts and regions. The main purpose of our model is its use to solve specific problems that require the detection and segmentation of human's body parts, for example, in user authentication.