Individualised model of facial age synthesis based on constrained regression

2020

Vis Comput

Techniques for facial age progression and regression have many applications and a myriad of challenges. As such, automatic aged or de-aged face generation has become an important subject of study in recent times. Over the past decade or so, researchers have been working on developing face processing mechanisms to tackle the challenge of generating realistic aged faces for applications related to smart systems. In this paper, we propose a novel approach to try and address this problem. We use template faces based on the formulation of an average face of a given ethnicity and for a given age. Thus, given a face image, the target aged image for that face is generated by applying it to the relevant template face image. The resulting image is controlled by two parameters corresponding to the texture and the shape of the face. To validate our approach, we compute the similarity between aged images and the corresponding ground truth via face recognition. To do this, we have utilised a pre-trained convolutional neural network based on the VGG-face model for feature extraction, and we then use well-known classifiers to compare the features. We have utilised two datasets, namely the FEI and the Morph II, to test, verify and validate our approach. Our experimental results do suggest that the proposed approach achieves accuracy, efficiency and possess flexibility when it comes to facial age progression or regression.

Section: Literature Reviewmentioning

confidence: 99%

A framework for facial age progression and regression using exemplar face templates

Elmahmudi

2020

Vis Comput

“…Furthermore, motivated by the fact that each year the police record approximately 300,000 missing person cases in the U.K. alone (3), we endeavor to improve the current techniques of searching for missing people to achieve more successful outcomes. We present a novel age synthesis framework which revisits the approach we discussed in (4).…”

Section: Ben Needhammentioning

confidence: 99%

“…For each face, the feature vector c is decomposed into two orthogonal components, aging c age and identity c id components. The rational behind this is fully explained in (4). Hence c is expressed as,…”

Section: Bukar and Ugail Age Progression Of Ben Needham 1207mentioning

confidence: 99%

See 1 more Smart Citation

Facial Age Synthesis Using Sparse Partial Least Squares (The Case of Ben Needham)

Bukar

Journal of Forensic Sciences

2017

Automatic facial age progression (AFAP) has been an active area of research in recent years. This is due to its numerous applications which include searching for missing. This study presents a new method of AFAP. Here, we use an active appearance model (AAM) to extract facial features from available images. An aging function is then modelled using sparse partial least squares regression (sPLS). Thereafter, the aging function is used to render new faces at different ages. To test the accuracy of our algorithm, extensive evaluation is conducted using a database of 500 face images with known ages. Furthermore, the algorithm is used to progress Ben Needham's facial image that was taken when he was 21 months old to the ages of 6, 14, and 22 years. The algorithm presented in this study could potentially be used to enhance the search for missing people worldwide.

“…The i1i2i3 40 color transform has been used in the past to decorrelate the RGB channels using Karhunen-Loève transform. 39,41 Recently, quaternion, a powerful mathematical tool, has been applied to the problem. 42,43 This has proven to be a good feature extraction method due to its ability to preserve the spatial relationships among R, G, and B channels.…”

Section: Gender Classificationmentioning

confidence: 99%

Automatic age and gender classification using supervised appearance model

Bukar

Connah

2016

J. Electron. Imaging

YesAge and gender classification are two important problems that recently gained popularity in the\ud research community, due to their wide range of applications. Research has shown that both age and gender\ud information are encoded in the face shape and texture, hence the active appearance model (AAM), a statistical\ud model that captures shape and texture variations, has been one of the most widely used feature extraction\ud techniques for the aforementioned problems. However, AAM suffers from some drawbacks, especially when\ud used for classification. This is primarily because principal component analysis (PCA), which is at the core of\ud the model, works in an unsupervised manner, i.e., PCA dimensionality reduction does not take into account\ud how the predictor variables relate to the response (class labels). Rather, it explores only the underlying structure\ud of the predictor variables, thus, it is no surprise if PCA discards valuable parts of the data that represent discriminatory\ud features. Toward this end, we propose a supervised appearance model (sAM) that improves on AAM\ud by replacing PCA with partial least-squares regression. This feature extraction technique is then used for the\ud problems of age and gender classification. Our experiments show that sAM has better predictive power than the\ud conventional AAM