Deep and Ordinal Ensemble Learning for Human Age Estimation From Facial Images

Combined Deep Learning With Directed Acyclic Graph SVM for Local Adjustment of Age Estimation

“…In other words, in order to distinguish between young adults and the elderly, it is necessary to incorporate the age changes of facial soft tissue and skin into the study to analyze texture features [14]. The anthropometric model for age estimation is proposed by Mlinar [7] and became the main method of age estimation research in the 1990s. This model manually marks the facial feature points of the two-dimensional image, and measures the changes in the distance and proportion of the feature points to estimate the age [19].…”

“…To further verify the validity of the method, the results are compared with other age estimation methods based on deep learning, and the results are shown in TABLE II and Figure 8 and 9. [6] 4.67 / Levi [7] 3.85 / Lee [8] 3.80 3.74 Li [9] 3.73 / Singhal [10] 3.44 / Deep embedding method [49] 3.32 3.71 TF [50] 3.277 3.35 Apparent methods [51] 3.272 / Cluster-CNN [52] 3.24 3.43 LAAE 3.04(d=8) 3.17(d=4)…”

“…In TABLE II, the description of Wang [6], Levi [7], Lee [8], Li [9], Singhal [10] can be seen in the introduction. The remaining methods that not accounted are described below.…”

“…This method combines the residual network with the LSTM unit to construct the LSTM-ResNet network to extract the local features of the age-sensitive area, thereby effectively improving the accuracy of age estimation. Levi [7] adopted the decomposition idea and proposed to use two sets of classifications for depth and serial number combination learning. Specifically, they first establish an ensemble based on convolutional neural network (CNN) technology, and the serial number relationship is implicitly constructed by their basic learners.…”

Automated Road Safety Surveillance System using Hybrid CNN-LSTM Approach

“…The proposed model outperformed all the other standard algorithms applied on several benchmark datasets. Work done by C. Li et.al., [4] developed a new feature-selection technique for age estimation. Information regarding each feature, such as the local structure and ordinal information, is collected from the facial images.…”