Robust real-time face detection (original) (raw)

Adaboost and Other Face Detection Algorithms

Human intelligences can recollect and recognize a massive selection of faces, receiving a computer to do the same is challenging task but in modern domain there would be many practices of such systems. Face reorganization has been a fast increasing, challenging and interesting area in real time application. It can be extensively use for image and video processing; this requires computational models for the identification of the face. In this paper we will review the different methods for face recognition with Adaboost. This model should be easy and simple when implemented. Adaboost is based on haar cascade model and is quick and performs better in low resolution and complex background. It uses both approach of face detection i.e. image based and feature based. It is based on Robust Real Time Object Detection of Viola and Jone.

TRAINING ANALYSIS OF HAAR-CLASSIFIERS FOR FACE DETECTION

IAEME PUBLICATION, 2021

Haar features have been used in most of the works in literature as key components in the task of object as well as face detection. Training process of Haar features is an important step in the development of overall face detection system. A number of features can be observed in the training process of Haar features. In this work, we have done investigation in the training parameters during AdaBoost based machine learning of Haar features. Based on the studying of parameters during training process, efficient learners can be selected from a large pool of available features which are further cascaded to make the strong classifiers. Modification in face detection process by application of scaling of detector window by changing the base size of training samples and efficient detection technique has been proposed.