Facial image classification based on age and gender

Kalansuriya, Thakshila R.; Dharmaratne, Anuja

doi:10.1109/icter.2013.6761153

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…In relation to age demographics, image processing has focused mainly on facial age estimation. For instance, the work of [23] proposed a methodology for age and gender identification grounded on feature extraction from facial images. Classification is then done using neural networks according to the different shape and texture variations of wrinkles.…”

Section: Related Workmentioning

confidence: 99%

Coming to Grips with Age Prediction on Imbalanced Multimodal Community Question Answering Data

2021

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For almost every online service, it is fundamental to understand patterns, differences and trends revealed by age demographic analysis—for example, take the discovery of malicious activity, including identity theft, violation of community guidelines and fake profiles. In the particular case of platforms such as Facebook, Twitter and Yahoo! Answers, user demographics have impacts on their revenues and user experience; demographics assist in ensuring that the needs of each cohort are fulfilled via personalizing and contextualizing content. Despite the fact that technology has been made more accessible, thereby becoming evermore prevalent in both personal and professional lives alike, older people continue to trail Gen Z and Millennials in its adoption. This trailing brings about an under-representation that has a harmful influence on the demographic analysis and on supervised machine learning models. To that end, this paper pioneers attempts at examining this and other major challenges facing three distinct modalities when dealing with community question answering (cQA) platforms (i.e., texts, images and metadata). As for textual inputs, we propose an age-batched greedy curriculum learning (AGCL) approach to lessen the effects of their inherent class imbalances. When built on top of FastText shallow neural networks, AGCL achieved an increase of ca. 4% in macro-F1-score with respect to baseline systems (i.e., off-the-shelf deep neural networks). With regard to metadata, our experiments show that random forest classifiers significantly improve their performance when individuals close to generational borders are excluded (up to 20% more accuracy); and by experimenting with neural network-based visual classifiers, we discovered that images are the most challenging modality for age prediction. In fact, it is hard for a visual inspection to connect profile pictures with age cohorts, and there are considerable differences in their group distributions with respect to meta-data and textual inputs. All in all, we envisage that our findings will be highly relevant as guidelines for constructing assorted multimodal supervised models for automatic age recognition across cQA platforms.

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Section: Related Workmentioning

confidence: 99%

Coming to Grips with Age Prediction on Imbalanced Multimodal Community Question Answering Data

2021

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“…Kalansuriya and Dharmaratn 12 proposed an approach to classify facial images into their corresponding gender and age. It can be done by applying the training and learning process of the human brain in pattern recognition and classification from the normal computer.…”

Section: Related Workmentioning

confidence: 99%

Face and age recognition using three-dimensional discrete wavelet transform and rotational local binary pattern with radial basis function support vector machine method

Sonal¹,

Singh²,

Kant

2021

International Journal of Electrical Engineering & Education

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Interest in facial recognition hypotheses and algorithms has grown steadily over the last few decades. Video monitoring, criminal identification, building access control, and unmanned and autonomous vehicles are only a few examples of concrete applications that are becoming increasingly attractive to industry. Various techniques are being developed, including local, holistic, and hybrid approaches, which use only a few face image characteristics or the entire facial features to provide a face image description. Many methods have good results, if there are sufficiently representative training samples per person, in the face recognition system. Facial part finding and extraction show the utmost vital role in face and age recognition. In this research work a new algorithm is proposed for Face and Age Recognition (FAR) by using Discrete Wavelet Transform (DWT), Radial Basis Function Support Vector Machine (RBF-SVM) classifier, and Rotational Local Binary Pattern (RLBP). RLBP is utilized for the selection and extraction of features from the face image. In this algorithm, extract the face component like Nose, Mouth, Left and Right eye. In the preprocessing stage median filter is used to remove noises from the face image. By using this, there is an improvement in the feature extraction procedure. In pattern recognition, a basic errand is finding a picture from the picture parts. For the implementation of results FG-NET ((Face and Gesture Recognition Network) and AT&T datasets are used. The detection rate of face recognition has reached up to 92–98% and the detection rate for age recognition is 87%. The proposed algorithm is compared with SVM shows better over previous algorithms and also estimate the value of accuracy.

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“…Thus, the top k values are set to 1 and the remaining (8-k) bits are set to 0. The equation for obtaining the LDP code is shown in (1). …”

Section: A Local Directional Patternmentioning

confidence: 99%

“…Face classification is a technique that segregates facial images into different groups based on criteria such as age [1], [2], gender [3], [4] and facial expressions [5]. It is widely used in several biometric application domains such as access control, surveillance, identification systems, etc.…”

Section: Introductionmentioning

confidence: 99%

Natural vs Artificial Face Classification Using Uniform Local Directional Patterns and Wavelet Uniform Local Directional Patterns

D'Souza

Yampolskiy

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops

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Face classification is a technique used in Biometrics to help distinguish between facial images. However, this technique has been applicable on human face images only. Online virtual worlds such as Second Life, Sims Online, etc. are gaining popularity over the Internet. They require human users to create a digital persona of oneself, known as an "avatar". Several avatars are designed to resemble human users. With crime being reported in virtual worlds, computergenerated avatar faces being created from human faces and human-resembling humanoids being designed, there is a need to distinguish between natural and artificial faces. Our work applies two new face classification techniques on grayscale, facial images of humans and avatars to tell them apart. (1) Uniform Local Directional Pattern (ULDP) utilizes the uniform patterns from Local Directional Pattern (LDP) (2) Wavelet Uniform Local Directional Pattern (WULDP) applies the ULDP technique on the wavelet transform of an image. Extensive experiments conducted on five different face image datasets (Caltech, FERET for human faces and Entropia, Second Life, Evolver for avatar faces) achieve baseline average classification accuracies of 98.55% using ULDP and 89.55% using WULDP respectively.

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Facial image classification based on age and gender

Cited by 8 publications

References 6 publications

Coming to Grips with Age Prediction on Imbalanced Multimodal Community Question Answering Data

Coming to Grips with Age Prediction on Imbalanced Multimodal Community Question Answering Data

Face and age recognition using three-dimensional discrete wavelet transform and rotational local binary pattern with radial basis function support vector machine method

Natural vs Artificial Face Classification Using Uniform Local Directional Patterns and Wavelet Uniform Local Directional Patterns

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