A hybrid technique for face detection in color images

In this paper, a hybrid technique for face detection in color images is presented. The proposed technique combines three analysis models, namely skin detection, automatic eye localization, and appearance-based face/nonface classification. Using a robust histogram-based skin detection model, skin-like pixels are first identified in the RGB color space. Based on this, face bounding-boxes are extracted from the image. On detecting a face bounding-box, approximate positions of the candidate mouth feature points are identified using the redness property of image pixels. A region-based eye localization step, based on the detected mouth feature points, is then applied to face bounding-boxes to locate possible eye feature points in the image. Based on the distance between the detected eye feature points, face/non-face classification is performed over a normalized search area using the Bayesian discriminating feature (BDF) analysis method. Some subjective evaluation results are presented on images taken using digital cameras and a Webcam, representing both indoor and outdoor scenes

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications

Identifying person re-occurrences for personal photo management applications

Automatic identification of "who" is present in individual digital images within a photo management system using only content-based analysis is an extremely difficult problem. The authors present a system which enables identification of person reoccurrences within a personal photo management application by combining image content-based analysis tools with context data from image capture. This combined system employs automatic face detection and body-patch matching techniques, which collectively facilitate identifying person re-occurrences within images grouped into events based on context data. The authors introduce a face detection approach combining a histogram-based skin detection model and a modified BDF face detection method to detect multiple frontal faces in colour images. Corresponding body patches are then automatically segmented relative to the size, location and orientation of the detected faces in the image. The authors investigate the suitability of using different colour descriptors, including MPEG-7 colour descriptors, color coherent vectors (CCV) and color correlograms for effective body-patch matching. The system has been successfully integrated into the MediAssist platform, a prototype Web-based system for personal photo management, and runs on over 13000 personal photos

Non-intrusive Head Movement Analysis of Videotaped Seizures of Epileptic Origin

Abstract — In this work we propose a non-intrusive video analytic system for patient’s body parts movement analysis in Epilepsy Monitoring Unit. The system utilizes skin color modeling, head/face pose template matching and face detection to analyze and quantify the head movements. Epileptic patients’ heads are analyzed holistically to infer seizure and normal random movements. The patient does not require to wear any special clothing, markers or sensors, hence it is totally nonintrusive. The user initializes the person-specific skin color and selects few face/head poses in the initial few frames. The system then tracks the head/face and extracts spatio-temporal features. Support vector machines are then used on these features to classify seizure-like movements from normal random movements. Experiments are performed on numerous long hour video sequences captured in an Epilepsy Monitoring Unit at a local hospital. The results demonstrate the feasibility of the proposed system in pediatric epilepsy monitoring and seizure detection. I

Analisa Sistem Deteksi Wajah Menggunakan Segmentasi, Eigenface dan Template Matching

ABSTRAKSI: Deteksi wajah merupakan langkah awal pengenalan wajah manusia (Human Recognition Face). Warna merupakan ciri yang menonjol dari wajah manusia. Dengan menggunakan warna kulit sebagai ciri primitif untuk deteksi region wajah memiliki beberapa keuntungan antara lain dalam pemrosesan warna kebanyakan lebih cepat daripada pemrosesan ciri wajah yang lain. Untuk merepresentasikan wajah manusia dilakukan segmentasi kulit berdasarkan warna kulit untuk mensegmentasi skin region dan non-skin region dalam citra berwarna (Color Image). Hasil dari segmentasi kemudian akan dianalisa dengan Connected Component Analysis untuk menganalisa hubungan skin region dan mengidentifikasikan kandidat wajah. Selanjutnya dilakukan tahap Eigenface untuk mendefinisikan ciri-ciri penting yang merepresentasikan sekumpulan pola wajah, untuk digunakan dalam meminimasi non-skin region. Tahap akhir dari proses deteksi wajah ini adalah deteksi wajah dengan menggunakan metoda Template Matching untuk membandingkan citra kandidat wajah dengan template wajah, lalu meyakinkan tingkat kemiripan dengan menghitung nilai korelasinya dan menyimpulkan apakah wajah atau bukan wajah. Hasil dari proses ini adalah gambar yang berupa bagian wajah manusia dan informasi lain seperti jumlah wajah yang terdeteksi pada citra inputan, waktu proses, jumlah skin region. Pada tugas akhir ini, pengujian dilakukan terhadap 113 citra input masing-masing 73 citra dengan photo single dan 40 citra dengan photo group. Dari data hasil uji bahwa deteksi dengan penerapan ketiga teknik di atas di peroleh tingkat keberhasilan deteksi sebesar 98,6% untuk photo single dan 57,5% untuk photo group .Kata Kunci : Deteksi Wajah, Eigenface, Connected Component Analysis, Segmentasi, Template Matching, Warna kulitABSTRACT: Face detection is the first phase of human face recognition. Color is a feature appearing from human face. By using skin color as primitive feature to detect face region has some advantage for example in most of color processing are faster than other face feature processing. To represent human face, it is done by skin segmentation phase based on skin color to segment skin region and non- skin region in color images. The result of segmentation phase will be analyzed by Connected Component Analysis phase to analyze relationship of skin region and to identificate face candidate. Furthermore, Eigenface is applied to define the significant features that represent a set of face pattern to be used to minimize non – skin region. The final phase is face detection phase using Template Matching method to compare image of face candidate with face template, then to convince the similarity level by computing the correlation value and to summarize whether face or non-face. The result is an image of human face and other information as number of detected face in input image, processing time, number of skin region. In this final project, the testing is applied on 113 input images each of them, 73 for image with single photo and 40 for image with group photo. From the result data, the detection with three method gave 98,6% of success in detection for single photo and 57,5% for group photo.Keyword: Face Detection, Eigenface, Connected Component Analysis, Segmentation, Template Matching, Skin Colo

Fair comparison of skin detection approaches on publicly available datasets

Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In this work, we investigate the most recent researches in this field and we propose a fair comparison among approaches using several different datasets. The major contributions of this work are an exhaustive literature review of skin color detection approaches, a framework to evaluate and combine different skin detector approaches, whose source code is made freely available for future research, and an extensive experimental comparison among several recent methods which have also been used to define an ensemble that works well in many different problems. Experiments are carried out in 10 different datasets including more than 10000 labelled images: experimental results confirm that the best method here proposed obtains a very good performance with respect to other stand-alone approaches, without requiring ad hoc parameter tuning. A MATLAB version of the framework for testing and of the methods proposed in this paper will be freely available from https://github.com/LorisNann

Pengenalan Wajah Menggunakan Metode Fisherface

This paper describes human identification using fisherface method to identify someone. The output is whether recognized or not an input image as an individual in the database. There are four main stages for this method, mainly face detection, PCA (Principal Component Analysis) calculation, FLD (Fisher's Linear Analysis) calculation and classification stage. In face detection stage, color thresholding is used to segment pixels that contain skin color. PCA calculation and FLD calculation stages are used to form a set of fisherfaces from a training set or database that will be used. All face images can be reconstructed from the combination of fisherfaces with different weights for each face image. The last stage, classification stage, is to identify the input image by comparing the weight of fisherface required to reconstruct the input face towards face images in the training set. The weight calculation is done by using Euclidian distance method. The simulations are done for 66 input images and the successful recognition rate is about 81.82%