Sliding window protocol

Epistemology

Operating system

Machine learning

Architecture

Philosophy

Sociology

Pattern recognition (psychology)

Visual arts

Artificial intelligence

Mobile device

Simplicity

Window (computing)

Face (sociological concept)

Facial recognition system

Computer vision

Social science

Computer science

Face detection

Convolutional neural network

IEEE Transactions on Industrial Informatics

In this article, we propose a model of face detection in risk situations to help rescue teams speed up the search of people who might need help. The proposed lightweight convolutional neural network (CNN) architecture is designed to detect faces of people in mines, avalanches, under water, or other dangerous situations when their face might not be very visible over surrounding background. We have designed a novel light architecture cooperating with the proposed sliding window procedure. The designed model works with maximum simplicity to support mobile devices. An output from processing presents a box on face location in the screen of device. The model was trained by using Adam and tested on various images. Results show that proposed lightweight CNN detects human faces over various textures with accuracy above 99% and precision above 98% what proves the efficiency of our proposed model.

Lightweight Convolutional Neural Network Model for Human Face Detection in Risk Situations