A hierarchical neural network for human face detection

1996 ◽  
Vol 29 (5) ◽  
pp. 781-787 ◽  
Author(s):  
Paul Juell ◽  
Ron Marsh
Keyword(s):  
Neural Network ◽  
Face Detection ◽  
Human Face ◽  
Human Face Detection ◽  
Hierarchical Neural Network

scholarly journals HUMAN FACE DETECTION METHODS USING COMBINED CASCADE OF CLASSIFIERS

2014 ◽  
pp. 114-125
Author(s):  
Ihor Paliy
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Detection Methods ◽  
Classification Error ◽  
Asymmetric Structure ◽  
Human Face ◽  
High Detection Rate ◽  
Structure Generation ◽  
Human Face Detection

The paper presents the improved human face detection method using the combined cascade of classifiers with the improved face candidates’ verification approach, as well as methods and algorithms for the verification level (convolutional neural network) structure generation and training. The combined cascade shows a high detection rate with a very small number of false positives and the proposed candidates’ verification approach is in almost 3 times faster in comparison with the classic verification scheme. The network’s structure generation method allows creating the sparse asymmetric structure of the convolutional neural network automatically. The improved training method uses the adaptive training examples ratio to obtain a trained network with a very low classification error for the positive examples.

scholarly journals KLASIFIKASI USIA DENGAN CITRA PADA REGISTRASI GAME ONLINE

2019 ◽  
Vol 5 (3) ◽  
Author(s):  
Ni Komang Sri Julyantari ◽  
Rukmi Sari Hartati ◽  
Made Sudarma
Keyword(s):  
Neural Network ◽  
Face Detection ◽  
Online Games ◽  
Training Data ◽  
Human Face ◽  
Online Game ◽  
Human Face Detection ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Backpropagation Artificial Neural Network

ABSTRACT The importance of the biggest age that is utilized in sharing information technology is one of them is registering online games. Nowadays the proliferation of online games among the wider community is no exception in the class of children who fall into the category of not enough age. When playing online games, players are required to register early to log in. In registering the player is realized to enter the date of birth and this can be manipulated by players who really should be old enough or do not deserve to play online games. To overcome the falsehood or manipulation of data, the player who will register can provide a format photo. JPG with this photo issued the system to be built to know us really from the person who made the registration. To be able to perform face detection and age classification using the Viola Jones method which will be used for human face detection and the backpropagation artificial neural network method is used for classification. In this study the data used is https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/ by entering 100 data as test data and 200 as training data, then the findings obtained are 75% appropriate results Keywords: Age Classification, Online Game, Face Recognition<br />ABSTRAK<br />Pentingnya usia banyak dimanfaatkan dalam berbagi bidang teknologi informasi salah satunya adalah. Saat ini maraknya game online dikalangan masyarakat luas tidak terkecuali di kalangan anak-anak yang termasuk dalam kategori belum cukup umur. Pada saat melakukan permainan game online, pemain wajib melakukan register awal untuk dapat melakukan login. Dalam melakukan register pemain diwajibkan untuk memasukkan tanggal lahir dan hal ini dpat dimanipulasi oleh pemain yang memang seharunya cukup umur atau belum pantas memainkan game online. Untuk mengatasi kepalsuan atau manipulasi data maka pemain yang akan melakukan register dapat memberikan foto dengan format. JPG dengan foto ini nantinya system yang akan dibangun dapat mengenal usis sebenarnya dari orang yang melakukan register tersebut. Untuk dapat melakukan deteksi wajah dan klasifikasi usia menggunakan metode viola jones yang akan digunakan untuk deteksi wajah manusia dan metode jaringan saraf tiruan backpropagation digunakan untuk klasifikasi. Dalam penelitian ini data yang digunakan 100 data sebagai data uji dan 200 sebagai data latih dari https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/, maka hasil akurasi yang didapat adalah 75% hasil sesuai.<br />Kata kunci : Kalsifikasi Usia, Game Online, Deteksi Wajah

AN EFFICIENT APPROACH FOR FACE DETECTION AND FACIAL FEATURE LOCATION USING PRUNE-AND-SEARCH TECHNIQUE

2004 ◽  
Vol 18 (06) ◽  
pp. 987-1005 ◽  
Author(s):  
CHIN-CHEN CHANG ◽  
YUAN-HUI YU
Keyword(s):  
Face Detection ◽  
Facial Feature ◽  
Facial Features ◽  
Base Line ◽  
Human Face ◽  
Feature Location ◽  
Search Technique ◽  
Efficient Approach ◽  
Human Face Detection ◽  
Geometric Knowledge

This paper proposes an efficient approach for human face detection and exact facial features location in a head-and-shoulder image. This method searches for the eye pair candidate as a base line by using the characteristic of the high intensity contrast between the iris and the sclera. To discover other facial features, the algorithm uses geometric knowledge of the human face based on the obtained eye pair candidate. The human face is finally verified with these unclosed facial features. Due to the merits of applying the Prune-and-Search and simple filtering techniques, we have shown that the proposed method indeed achieves very promising performance of face detection and facial feature location.

scholarly journals Normal and Abnormal Human Face Detection Based on DCT and FFT Techniques - A Proposed Method

2021 ◽  
Author(s):  
Samir Bandyopadhyay ◽  
Shawni Dutta ◽  
Vishal Goyal ◽  
Payal Bose
Keyword(s):  
Face Detection ◽  
Fourier Transformation ◽  
Input Image ◽  
The Other ◽  
Fast Fourier Transformation ◽  
Length Variation ◽  
Human Face ◽  
Discrete Cosine Transformation ◽  
Cosine Transformation ◽  
Human Face Detection

In today&rsquo;s world face detection is the most important task. Due to the chromosomes disorder sometimes a human face suffers from different abnormalities. For example, one eye is bigger than the other, cliff face, different chin-length, variation of nose length, length or width of lips are different, etc. For computer vision currently this is a challenging task to detect normal and abnormal face and facial parts from an input image. In this research paper a method is proposed that can detect normal or abnormal faces from a frontal input image. This method used Fast Fourier Transformation (FFT) and Discrete Cosine Transformation of frequency domain and spatial domain analysis to detect those faces.

scholarly journals Human Face Detection in Color Images

2021 ◽  
Author(s):  
Jun Gao
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Video Retrieval ◽  
Color Images ◽  
Human Face ◽  
Detection Scheme ◽  
Retrieval Systems ◽  
Human Face Detection ◽  
The Face ◽  
Video Retrieval Systems

Detection of human face has many realistic and important applications such as human and computer interface, face recognition, face image database management, security access control systems and content-based indexing video retrieval systems. In this report a face detection scheme will be presented. The scheme is designed to operate on color images. In the first stage of algorithm, the skin color regions are detected based on the chrominance information. A color segmentation stage is then employed to make skin color regions to be divided into smaller regions which have homogenous color. Then, we use the iterative luminance segmentation to further separate the detected skin region from other skin-colored objects such as hair, clothes, and wood, based on the high variance of the luminance component in the neighborhood of edges of objects. Post-processing is applied to determine whether skin color regions fit the face constrains on density of skin, size, shape and symmetry and contain the facial features such as eyes and mouths. Experimental results show that the algorithm is robust and is capable of detecting multiple faces in the presence of a complex background which contains the color similar to the skin tone.

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