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

Survey on face detection methods

Face detection has attracted attention from many researchers due to its wide range of applications such as video surveillance, face recognition, object tracking and expression analysis. It consists of three stages which are preprocessing, feature extraction and classification. Firstly, preprocessing is the process of extracting regionsfrom images or real-time web camera, which then acts as a face or non-face candidate images. Secondly, feature extraction involves segmenting the desired features from preprocessed images. Lastly, classification is a process of clustering extracted features based on certain criteria. In this paper, 15 papers published from year 2013 to 2018 are reviewed. In general, there are seven face detection methods which are Skin Colour Segmentation, Viola and Jones, Haar features, 3D-mean shift, Cascaded Head and Shoulder detection (CHSD), and Libfacedetection. The findings show that skin colour segmentation is the most popular method used for feature extraction with 88% to 98% detection rate. Unlike skin colour segmentation method, Viola and Jones method mostly comprise of face regions and other parts of human body with 80% to 90% detection rate. OpenCV, Python or MATLAB can be used to develop real-life face detection system

Adaptive Method for Improvement of Human Skin Detection in Colour

In this paper a new approach to detect human skin in colour images is proposed. The method uses the classification of the three colour components of the RGB system (Red, Green and Blue), with a new approach to skin classifiers and face detection. The developed approach uses an adaptive methodology embedded in the skin classifier algorithm and a new face detection method to determine the location of the face in the image, improving the detection of the skin pixels and therefore reducing simultaneously the computational burden. The developed adaptive method varies the parameters of the base detection algorithm, for each one of the RGB colour components, in order to reduce the influence of external disturbances, namely the different illumination conditions. Experimental tests validate the proposed methodology showing very good results, in terms of skin detection with very different characteristics in face morphology, different backgrounds and illumination conditions

Component-based Face Detection in Colour Images

Abstract: Face detection is an important process in many applications such as face recognition, person identification and tracking, and access control. The technique used for face detection depends on how a face is modelled. In this paper, a face is defined as a skin region and a lips region that meet certain geometrical criteria. Thus, the face detection system has three main components: a skin detection module, a lips detection module, and a face verification module. The Multi-layer perceptron (MLP) neural networks was used for the skin and lips detection modules. In order to test the face detection system, two databases were created. The images in the first database, called In-house, were taken under controlled environment while those in the second database, called WWW, were collected from the World Wide Web and as such have no restriction on lighting, head pose or background. The system achieved a correct detection rate of 87 and 80 percent on the In-house and WWW databases respectivel

Feature based face detection for unconstrained images

Face detection for unconstrained images often encounter issues like background variation, pose variation, facial expression, occlusion and noise. Face detection utilises two main methods; feature based and image based methods. The feature based method benefits from rotation independence, scale independence and quick execution time as compared to the image based method. Feature based method utilises skin colour, facial and blob features. Current research on feature based method often emphasises on Viola Jones (V-J) face detection and is only limited to the in-plane rotation of positive or negative forty-five degrees. However, the utilization of V-J face detection with the inclusion of noise is a challenge because the image of other objects will often be mistaken for faces thus resulting in false detections. This thesis focuses on pose variation and noise challenges of unconstrained images and will cover three techniques for V-J face detection for unconstrained images, namely the combination of V-J face detection with rotation enhancements, Bicubic interpolation and ratio Scale Invariant Feature Transform (SIFT). In this thesis, these three techniques play different roles in face detection. The first technique begins with the rotation of the image file at thirty degree steps until it reaches a total rotation of three hundred and sixty degrees. At each thirty degree step, V-J face detection is applied, which in turn covers more angles of a rotated face. The second technique, Bicubic interpolation, corrects distorted images. The third technique, ratio SIFT, is a proposed post-processing to eliminate false detection for unconstrained images. Robust feature detection in scaling and invariant rotation is utilised in the above techniques to aid in the detecting of faces in images. Different face detections have been recommended for the unconstrained grey images and unconstrained colour images respectively with in-plane rotations and some with multiple faces. The images utilised for testing and evaluation in this thesis originated from Carnegie Mellon University (CMU) unconstrained grey images with in-plane rotations and Face Detection Data Set and Benchmark (FDDB) unconstrained colour images with multiple faces datasets. Fifty CMU datasets with twelve rotations on each image and various permutations resulted in six hundred test pattern images have been performed. Furthermore, another six hundred test pattern images from FDDB were also evaluated. These images have been measured through correct detection rate, true positive and false positive. The results from these measurements indicate that the proposed feature based face detection technique, focused on the V-J face detection method, for unconstrained images has the ability to detect rotated faces with high detection accuracy which in turn reduces false detections. In conclusion, the proposed enhancements will improve the current V-J face detection technique and overcome future challenges for unconstrained images

GESTURE RECOGNITION SYSTEM

In this paper, the hand gesture of a person is recognised and it identifies which hand of the person is raised. The skin colour is taken to recognise hands and face and the dark background is taken so that the skin detection may become easier. The hands and face are differentiated on the basis of area and centroid. Camera is the only input device used in this algorithm. No other input device is used to differentiate hands from the remaining body. This algorithm can be used both on the captured images and real time images

Skin Colour Detection Based On An Adaptive Multi-Thresholding Technique

Today, human region detection in complex scenes has received a great attention due to the wide use of websites and the considerable progress of the still and video images processing tasks. Skin detection or segmentation is a very popular and useful technique for detecting and tracking of human body parts, especially faces and hands. It is employed in tasks like face or hand detection and tracking, filtering of objectionable web images, people retrieval in databases and the Internet. This thesis aims to build a skin detection system that will discriminate between the skin and non-skin pixels in still coloured images. This is done by introducing a metric, which measures the distances of the pixel colour to skin tone. The need for a compact skin model representation stimulates the development of parametric skin distribution models which is used in this research.An adaptive skin colour detection model has been proposed in this thesis. The model is based on the bivariate normal distribution of the skin chromatic subspace. The model uses the 2D Single Gaussian model (SGM), and the 2D Gaussian mixture model (GMM) to represent the skin colour distribution. The model also based on the image segmentation using an automatic and adaptive multi-thresholding technique. This thesis shows that the Gaussian mixture model alone or the Gaussian single model does not improve the performance of the skin detection model due to the number of false detections for high correct classification. For this reason, a combination of SGM and GMM in the same model is proposed in this research. The results show that when processing images of different people taken in different imaging conditions, the use of only one single threshold value is not adapted, and since the proposed method is capable of adaptively adjusting its threshold values and effectively separating skin colour regions from non skin ones, it is applicable to images with various conditions. The experiment shows that the suggested algorithm achieves a noticeable performance improvement and offers a robust solution for skin detection under varying illumination. The results show that the average of the correct rate “True Positive” rate for the test images is equal to 94.064% while the False Positive average is equal to 13.166%

Face Detection in Complex Natural Scenes

Face detection is an important preliminary process for all other tasks with faces, such as expression analysis and person identification. It is also known to be rapid and automatic, which indicates that detection might utilise low-level visual information. It has been suggested that this consist of a ‘skin-coloured, face-shaped template’, while internal facial features, such as the eyes, nose and mouth might also help to optimise performance. To explore these ideas directly, this thesis first examined how shape and features are integrated into a detection template (Chapter 2). For this purpose, face content was isolated into three ranges of spatial frequency, comprising low (LSF), mid (MSF) and high (HSF) frequencies. Detection performance in these conditions was always compared with an original condition, which displayed unfiltered images in the full range of spatial frequency. Across five behavioural and eye-tracking experiments, detection was best for the original condition, followed by MSF, LSF and HSF faces. LSF faces, which provide only crude visual detail (i.e. gross colour shape), were detected as quickly as MSF faces but less accurate. In addition, LSF faces showed a clear advantage over HSF, which contains fine visual information (i.e. detailed lines of the eyes, nose, and mouth), in terms of detection speed and accuracy. These findings indicate that face detection is driven by simple information, such as the saliency of colour and shape, which supports the notion of a skin-coloured faceshape template. However, the fast and more accurate performance for faces in the full and mid-spatial frequencies also indicates that facial features contribute to optimize detection. In Chapter 3, three further eye-tracking experiments are reported, which explore further whether the height-to-width ratio of a coloured-shape template might be important for detection. Performance was best when faces’ natural height-to-width ratios were preserved compared to vertically and horizontally stretched faces. This indicates that this is an important element of the cognitive template for face template. The results also highlight that face detection differs from face recognition, which tolerates the same type of geometric disruption. Based on the results of Chapter 2 and 3, a model of face detection is proposed in Chapter 4. In this model, colour face-shape and features drive detection in parallel, but not necessarily at equal speed, in a “horse race”. Accordingly, rapid detection is normally driven by salient colour and shape cues that preserve the height-to-width ratio of faces, but finer visual detail from features can facilitate this process when further information is needed

Face and Object Recognition and Detection Using Colour Vector Quantisation

In this paper we present an approach to face and object detection and recognition based on an extension of the contentbased image retrieval method of Lu and Teng (1999). The method applies vector quantisation (VQ) compression to the image stream and uses Mahalonobis weighted Euclidean distance between VQ histograms as the measure of image similarity. This distance measure retains both colour and spatial feature information but has the useful property of being relatively insensitive to changes in scale and rotation. The method is applied to real images for face recognition and face detection applications. Tracking and object detection can be coded relatively efficiently due to the data reduction afforded by VQ compression of the data stream. Additional computational efficiency is obtained through a variation of the tree structured fast VQ algorithm also presented here