Facial Feature Extraction and Applications: A Review

Wu, Yingming; Wang, Hsueh-Wu; Lu, Yen-Ling; Yen, Shin Y.; Hsiao, Ying-Tung

doi:10.1007/978-3-642-28487-8_23

Cited by 9 publications

(2 citation statements)

References 12 publications

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“…Face detection and facial feature extraction are significant for the face tracking, facial expression recognition and face recognition. Facial feature extraction plays a crucial role in the areas of human computer interaction [1], video monitoring and person identification [2]. Age and gender classification algorithms are mainly based on the identification of facial features [3].…”

Section: Introductionmentioning

confidence: 99%

Face Detection and Facial Feature Extraction with Machine Learning

Karahan

Lacinkaya

Erdonmez

et al. 2021

Lecture Notes in Networks and Systems

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Face detection is important part of surveillance systems and it has been widely used in computer vision and image processing. Face detection is also first step of the facial feature extraction. Facial feature extraction is a topic that has been focused on by many researchers in computer science, psychology, medicine and related fields and has become increasingly important in recent years. With the help of facial features, machine learning algorithms can estimate ages and classify genders of people. In this paper, face detection, facial feature extraction, age estimation and gender classification are presented. Firstly, face detection and extraction of facial features like eyes, eyebrows, mouth and nose are presented. Secondly, age estimation and gender classification based on the extracted facial features are explained. Experimental results prove that face detection algorithm efficiently detects human faces and facial feature algorithm accurately locates eyes, eyebrows, mouth and nose. Experimental results also show that, based on the extracted facial features, convolutional neural network architecture estimates ages of the people and classifies their gender.

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Section: Introductionmentioning

confidence: 99%

Face Detection and Facial Feature Extraction with Machine Learning

Karahan

Lacinkaya

Erdonmez

et al. 2021

Lecture Notes in Networks and Systems

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“…This Technology is utilized in varied range of applications, such as human identification, access control and security investigation. The core of any biometric system depends on the accurate extracting features and classification, thus facial features detection and classification are a major concern in various applications such as face recognition, human computer interaction, surveillance systems, facial animation and facial expression [1]. About 300 papers focused on general facial feature extraction regarding different methods, some works applied handcrafted features, other employed statistical learning works reviewed by Wang et al [2].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method of Face Feature Extraction, Classification Based on MLBP and Layered-Recurrent Network

Al-Qaisi¹,

Al-Tarawneh²,

ElSaid³

et al. 2020

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Face feature extraction and classification is an attracting research area for its various applications. This paper proposes a hybrid technique based on modified local binary pattern (MLBP) and Layered-Recurrent neural network (L-RNN) to recognize the human faces. The proposed MLBP algorithm reduces the dimensions of extracted face images features. The classification process is conducted using L-RNN. The quasi-Newton back propagation algorithm is used to train the L-RNN. The proposed hybrid technique is examined on MUCT database and the performance analysis of different ANN techniques shows that the Hybrid technique is robust and has superior performance over conventional methods. It achieves the best classification rate of 98%.

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