Using Ranking-CNN for Age Estimation

Liu³

et al. 2018

WIREs Data Min & Knowl

The salient facial feature discovery is one of the important research tasks in ethnical group face recognition. In this paper, we first construct an ethnical group face dataset including Chinese Uyghur, Tibetan, and Korean. Then, we show that the effective sparse sensing approach to general face recognition is not working anymore for ethnical group facial recognition if the features based on whole face image are used. This is partially due to a fact that each ethnical group may have its own characteristics manifesting only in specified face regions. Therefore, we will analyze the particularity of three ethnical groups and aim to find the common characterizations in some local regions for the three ethnical groups. For this purpose, we first use the facial landmark detector STASM to find some important landmarks in a face image, then, we use the well‐known data mining technique, the mRMR algorithm, to select the salient geometric length features based on all possible lines connected by any two landmarks. Second, based on these selected salient features, we construct three “T” regions in a face image for ethnical feature representation and prove them to be effective areas for ethnicity recognition. Finally, some extensive experiments are conducted and the results reveal that the proposed “T” regions with extracted features are quite effective for ethnical group facial recognition when the L2‐norm is adopted using the sparse sensing approach. In comparison to face recognition, the proposed three “T” regions are evaluated on the olivetti research laboratory face dataset, and the results show that the constructed “T” regions for ethnicity recognition are not suitable for general face recognition. This article is categorized under: Algorithmic Development > Structure Discovery Algorithmic Development > Biological Data Mining Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Classification

Section: Preliminariesmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Expression of Concern: Facial feature discovery for ethnicity recognition

Wang

Liu³

et al. 2018

WIREs Data Min & Knowl

“…Inspired by two ranking-based methods [3,22], we decompose the ordinal regression into a series of binary classifications. Specifically, the ordinal regression with K ranks is decomposed into K − 1 binary classifiers {f k } K−1 k=1 .…”

Section: Ordinal Regressionmentioning

confidence: 99%

“…However, both of them do not consider the ordinal relationship between age labels, which is an important clue to age estimation. Therefore, the ranking-based methods for facialbased age estimation [3,5,13,22] are proposed to solve Figure 2. The predictive probability of k-th classifier is expected not greater than that of (k − 1)-th classifier on an ordinal distribution.…”

Section: Introductionmentioning

confidence: 99%

Ordinal distribution regression for gait-based age estimation

Zhu

et al. 2020

Sci. China Inf. Sci.

Computer vision researchers prefer to estimate the age from face images due to informative facial features. Estimating the age from face images becomes challenging when people are far away from camcorders or occluded. As the unique biometric feature that can be perceived efficiently at a distance, gait can be an alternative way to predict the age in case that face images are not available. However, existing gait-based classification or regression methods ignore the ordinal relationship of different ages, which is an important clue to the age estimation. In this paper, we propose an ordinal distribution regression with a global and local convolutional neural network for gait-based age estimation. Specifically, we decompose the gait-based age regression into a series of binary classifications to incorporate the ordinal information of the age. Then an ordinal distribution loss is proposed to take inner relationship among these classifications into account by penalizing the distribution discrepancy between the estimated and the groundtruth. In addition, our neural network consists of a global and three local sub-networks, which is capable of learning the global structure and more local details from head, body and feet of gait, respectively. By comparing with the stateof-the-art methods of gait-based age estimation, this paper highlights, experimentally, that the proposed approach has a better predictive performance on the OULP-Age dataset.

“…[16]. More recently, CNN have been employed in face imagebased age and gender classification tasks [17][18][19]. However, as face images vary in a wide range under the unconstrained conditions (namely, in the wild), the performances of CNN still need to be improved, especially in age estimation tasks.…”

Section: Introductionmentioning

confidence: 99%

Landmark-Guided Local Deep Neural Networks for Age and Gender Classification

2018

Journal of Sensors

Many types of deep neural networks have been proposed to address the problem of human biometric identification, especially in the areas of face detection and recognition. Local deep neural networks have been recently used in face-based age and gender classification, despite their improvement in performance, their costs on model training is rather expensive. In this paper, we propose to construct a local deep neural network for age and gender classification. In our proposed model, local image patches are selected based on the detected facial landmarks; the selected patches are then used for the network training. A holistical edge map for an entire image is also used for training a "global" network. The age and gender classification results are obtained by combining both the outputs from both the "global" and the local networks. Our proposed model is tested on two face image benchmark datasets; competitive performance is obtained compared to the state-of-the-art methods.