The enhancement of active appearance model and active shape model in extracting facial features for age estimation - Iqtait, Musab M. M.
Age estimation through face image has a lot of potential computer applications, such as social media communication and access control. However, it is a challenging problem for the existing methods in computer systems to effectively estimate humanfacial age. There are many methods for age estimation, but they are significantly lacking in their performances, especially when compared to other related task performances in face recognition. Not only the reflection of face structure and face texture is incomplete, the complexity of feature has not been properly addressed. For this reason, facial features need to be highly compressed to avoid a high degree of complexity while accommodating larger users and increasing data relation. The objectives of this study are to develop a facial feature extra tion model using machine learning based approaches for extracting features from a human face and to map the features to an output label that estimates the predicted age (in years) of an individual. An integrated facial feature extraction model was de eloped which combines the power of both models, namely Active Appearance Mod 1 (AAM) and Active Shape Model (ASM), in order to obtain an innovative result on the problem of feature detection. A two-step app oach for combining AAM and ASM was suggested. In the first step, the ASM was utilized to locat the outer shap Ian marks of the face, and in the second step, AAM was utilized to locate the inner shape landmarks of the face. The experiments were tested on the publicly accessible MORPH and LFBW databases. The dataset consists of 490 training images and 210 test images per database. The developed algorithm was evaluated by measuring the performance of the facial feature extraction with respect to feature accuracy and error rates. A few prominent machine learning algorithms were then explored for age prediction. A hierarchical approach was developed to train the machine learning algorithms for facial features with age labels. Secondly, an expectation framework was developed to jointly address the issues of categorizing the test features into age labels based on the trained data. The performance was evaluated using Mean Absolute Error (MAE), Cumulative Score (CS) and processing time. The experimental results indicated that the proposed model is effective in extracting facial features and estimating age. For LFPW and MORPH databases, the error rates were 2.2628 and 2.7174 respectively. The best MAE value obtained from the proposed model is 2.94, achieved in Canonical Correlation Analysis (CCA). The best CS value obtained from the proposed model is 88.9%, achieved in CCA. The best processing time obtained from the proposed model is 0.036142, achieved in Support Vector Machine (SVM). These results are more promising when compared to several states of the art techniques like ASM and AAM, in which the error rates were 2.6607 and 3.1127 for the LFPW and 2.8801 and 2.0417 for the MORPH, respectively. The proposed model is proven to have shown the best results, and thus compensating shape and texture variations. The information obtained from these model yield semantic criteria to be used in content based image retrieval, further improvement can be made to make the proposed model even more robust and applicable.