Muhammad Azhar^1,*, Sehat Ullah¹, Khalil Ullah², Habib Shah³, Abdallah Namoun⁴, Khaliq Ur Rahman⁵

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 165-182, 2023, DOI:10.32604/cmc.2023.034605 - 06 February 2023

Abstract Human biometric analysis has gotten much attention due to its widespread use in different research areas, such as security, surveillance, health, human identification, and classification. Human gait is one of the key human traits that can identify and classify humans based on their age, gender, and ethnicity. Different approaches have been proposed for the estimation of human age based on gait so far. However, challenges are there, for which an efficient, low-cost technique or algorithm is needed. In this paper, we propose a three-dimensional real-time gait-based age detection system using a machine learning approach. The… More >

Sultan Alkaabi^1,*, Salman Yussof¹, Sameera Al-Mulla²

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2499-2516, 2023, DOI:10.32604/cmc.2023.029914 - 31 October 2022

Abstract Age estimation using forensics odontology is an important process in identifying victims in criminal or mass disaster cases. Traditionally, this process is done manually by human expert. However, the speed and accuracy may vary depending on the expertise level of the human expert and other human factors such as level of fatigue and attentiveness. To improve the recognition speed and consistency, researchers have proposed automated age estimation using deep learning techniques such as Convolutional Neural Network (CNN). CNN requires many training images to obtain high percentage of recognition accuracy. Unfortunately, it is very difficult to… More >

Valliappan Raman¹, Khaled ELKarazle^2,*, Patrick Then²

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 105-118, 2022, DOI:10.32604/iasc.2022.025608 - 15 April 2022

Abstract Facial age is one of the prominent features needed to make decisions, such as accessing certain areas or resources, targeted advertising, or more straightforward decisions such as addressing one another. In machine learning, facial age estimation is a typical facial analysis subtask in which a model learns the different facial ageing features from several facial images. Despite several studies confirming a relationship between age and gender, very few studies explored the idea of introducing a gender-based system that consists of two separate models, each trained on a specific gender group. This study attempts to bridge… More >

Syeda Amna Rizwan¹, Yazeed Yasin Ghadi², Ahmad Jalal¹, Kibum Kim^3,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5235-5252, 2022, DOI:10.32604/cmc.2022.023328 - 14 January 2022

Abstract With the advancement of computer vision techniques in surveillance systems, the need for more proficient, intelligent, and sustainable facial expressions and age recognition is necessary. The main purpose of this study is to develop accurate facial expressions and an age recognition system that is capable of error-free recognition of human expression and age in both indoor and outdoor environments. The proposed system first takes an input image pre-process it and then detects faces in the entire image. After that landmarks localization helps in the formation of synthetic face mask prediction. A novel set of features More >

Rawan Sulaiman Howyan^1,2,*, Emad Sami Jaha¹

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 555-580, 2022, DOI:10.32604/iasc.2022.019533 - 03 September 2021

Abstract Human face is one of the most widely used biometrics based on computer-vision to derive various useful information such as gender, ethnicity, age, and even identity. Facial age estimation has received great attention during the last decades because of its influence in many applications, like face recognition and verification, which may be affected by aging changes and signs which appear on human face along with age progression. Thus, it becomes a prominent challenge for many researchers. One of the most influential factors on age estimation is the type of features used in the model training… More >

Yang Wang¹, Ying Tian^1,*, Ou Tian²

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2203-2216, 2021, DOI:10.32604/cmc.2021.018709 - 21 July 2021

Abstract As the use of facial attributes continues to expand, research into facial age estimation is also developing. Because face images are easily affected by factors including illumination and occlusion, the age estimation of faces is a challenging process. This paper proposes a face age estimation algorithm based on lightweight convolutional neural network in view of the complexity of the environment and the limitations of device computing ability. Improving face age estimation based on Soft Stagewise Regression Network (SSR-Net) and facial images, this paper employs the Center Symmetric Local Binary Pattern (CSLBP) method to obtain the More >

Ameer A. Badr^1,2,*, Alia K. Abdul-Hassan²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1713-1729, 2021, DOI:10.32604/cmc.2021.016732 - 13 April 2021

Abstract Age estimation in short speech utterances finds many applications in daily life like human-robot interaction, custom call routing, targeted marketing, user-profiling, etc. Despite the comprehensive studies carried out to extract descriptive features, the estimation errors (i.e. years) are still high. In this study, an automatic system is proposed to estimate age in short speech utterances without depending on the text as well as the speaker. Firstly, four groups of features are extracted from each utterance frame using hybrid techniques and methods. After that, 10 statistical functionals are measured for each extracted feature dimension. Then, the… More >

Noor Mualla¹, Essam H Houssein^{2, *}, M. R. Hassan¹

CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 591-605, 2020, DOI:10.32604/cmc.2020.08580

Abstract Chronological age estimation using panoramic dental X-ray images is an essential task in forensic sciences. Various statistical approaches have proposed by considering the teeth and mandible. However, building automated dental age estimation based on machine learning techniques needs more research efforts. In this paper, an automated dental age estimation is proposed using transfer learning. In the proposed approach, features are extracted using two deep neural networks namely, AlexNet and ResNet. Several classifiers are proposed to perform the classification task including decision tree, k-nearest neighbor, linear discriminant, and support vector machine. The proposed approach is evaluated More >

Qing Tian^1,2,3,*, Meng Cao^1,2, Tinghuai Ma^1,2

CMC-Computers, Materials & Continua, Vol.56, No.3, pp. 467-482, 2018, DOI:10.3970/cmc.2018.02197

Abstract The research of human facial age estimation (AE) has attracted increasing attention for its wide applications. Up to date, a number of models have been constructed or employed to perform AE. Although the goal of AE can be achieved by either classification or regression, the latter based methods generally yield more promising results because the continuity and gradualness of human aging can naturally be preserved in age regression. However, the neighbor-similarity and ordinality of age labels are not taken into account yet. To overcome this issue, the cumulative attribute (CA) coding was introduced. Although such More >

Clyde K Coelho, Cristobal Hiche, Aditi Chattopadhyay

Structural Durability & Health Monitoring, Vol.7, No.1&2, pp. 65-82, 2011, DOI:10.3970/sdhm.2011.007.065

Abstract Low velocity impacts on composite plates often create subsurface damage that is difficult to diagnose. Fiber Bragg grating (FBG) sensors can be used to detect subsurface damage in composite laminates due to low velocity impact. This paper focuses on the prediction of impact loading in composite structures as a function of time using a support vector regression approach. A time delay embedding feature extraction scheme is used since it can characterize the dynamics of the impact using the sensor signals. The novelty of this approach is that it can be applied on complex geometries and… More >