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Browsing Computer Science by Author "Angulu, Raphael."

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    Gender classification using facial components.
    (2018) Bayana, Mayibongwe Handy.; Viriri, Serestina.; Angulu, Raphael.
    Gender classification is very important in facial analysis as it can be used as input into a number of systems such as face recognition. Humans are able to classify gender with great accuracy however passing this ability to machines is a complex task because of many variables such as lighting to mention a few. For the purpose of this research we have approached gender classification as a binary problem, involving the two classes male and female. Two datasets are used in this research which are the FG-NET dataset and Pilots Parliament datasets. Two appearance based feature extractors are used which are the LBP and LDP with the Active Shape model being included by fusing. The classifiers used here are the Support Vector Machine with Radial Basis Function kernel and an Artificial Neural Network with backpropagation. On the FG-NET an average detection of 90.6% against that of 87.5% to that of the PPB. Gender is then detected from the facial components the nose, eyes among others. The forehead recorded the highest accuracy with 92%, followed by the nose with 90%, cheeks with 89.2% and the eyes with 87% and the mouth recorded the lowest accuracy of 75%. As a result feature fusion is then carried out to improve classification accuracies especially that of the mouth and eyes with lowest accuracies. The eyes with an accuracy of 87% is fused with the forehead with 92% and the resulting accuracy is an increase to 93%. The mouth, with the lowest accuracy of 75% is fused with the nose which has an accuracy of 90% and the resulting accuracy is 87%. These results carried out by fusing through addition showed improved results. Fusion is then carried out between Appearance based and shape based features. On the FG-NET dataset using the LBP and LDP an accuracy of 85.33% and 89.53% with the PPB recording 83.13%, 89.3% for LBP and LDP respectively. As expected and shown by previous researchers the LDP clearly obtains higher classification accuracies as it than LBP as it uses gradient rather than pixel intensity. We then fuse the vectors of the LDP, LBP with that of the ASM and carry out dimensionality reduction, then fusion by addition. On the PPB dataset fusion of LDP and ASM records 81.56%, and 94.53% with the FG-NET recording 89.53% respectively.
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    Hierarchical age estimation using enhanced facial features.
    (2018) Angulu, Raphael.; Tapamo, Jules-Raymond.; Adewumi, Aderemi Oluyinka.
    Ageing is a stochastic, inevitable and uncontrollable process that constantly affect shape, texture and general appearance of the human face. Humans can easily determine ones’ gender, identity and ethnicity with highest accuracy as compared to age. This makes development of automatic age estimation techniques that surpass human performance an attractive yet challenging task. Automatic age estimation requires extraction of robust and reliable age discriminative features. Local binary patterns (LBP) sensitivity to noise makes it insufficiently reliable in capturing age discriminative features. Although local ternary patterns (LTP) is insensitive to noise, it uses a single static threshold for all images regardless of varied image conditions. Local directional patterns (LDP) uses k directional responses to encode image gradient and disregards not only central pixel in the local neighborhood but also 8 􀀀 k directional responses. Every pixel in an image carry subtle information. Discarding 8 􀀀 k directional responses lead to lose of discriminative texture features. This study proposes two variations of LDP operator for texture extraction. Significantorientation response LDP (SOR-LDP) encodes image gradient by grouping eight directional responses into four pairs. Each pair represents orientation of an edge with respect to central reference pixel. Values in each pair are compared and the bit corresponding to the maximum value in the pair is set to 1 while the other is set to 0. The resultant binary code is converted to decimal and assigned to the central pixel as its’ SOR-LDP code. Texture features are contained in the histogram of SOR-LDP encoded image. Local ternary directional patterns (LTDP) first gets the difference between neighboring pixels and central pixel in 3 3 image region. These differential values are convolved with Kirsch edge detectors to obtain directional responses. These responses are normalized and used as probability of an edge occurring towards a respective direction. An adaptive threshold is applied to derive LTDP code. The LTDP code is split into its positive and negative LTDP codes. Histograms of negative and positive LTDP encoded images are concatenated to obtain texture feature. Regardless of there being evidence of spatial frequency processing in primary visual cortex, biologically inspired features (BIF) that model visual cortex uses only scale and orientation selectivity in feature extraction. Furthermore, these BIF are extracted using holistic (global) pooling across scale and orientations leading to lose of substantive information. This study proposes multi-frequency BIF (MF-BIF) where frequency selectivity is introduced in BIF modelling. Local statistical BIF (LS-BIF) uses local pooling within scale, orientation and frequency in n n region for BIF extraction. Using Leave-one-person-out (LOPO) validation protocol, this study investigated performance of proposed feature extractors in age estimation in a hierarchical way by performing age-group classification using Multi-layer Perceptron (MLP) followed by within age-group exact age regression using support vector regression (SVR). Mean absolute error (MAE) and cumulative score (CS) were used to evaluate performance of proposed face descriptors. Experimental results on FG-NET ageing dataset show that SOR-LDP, LTDP, MF-BIF and LS-BIF outperform state-of-the-art feature descriptors in age estimation. Experimental results show that performing gender discrimination before age-group and age estimation further improves age estimation accuracies. Shape, appearance, wrinkle and texture features are simultaneously extracted by visual system in primates for the brain to process and understand an image or a scene. However, age estimation systems in the literature use a single feature for age estimation. A single feature is not sufficient enough to capture subtle age discriminative traits due to stochastic and personalized nature of ageing. This study propose fusion of different facial features to enhance their discriminative power. Experimental results show that fusing shape, texture, wrinkle and appearance result into robust age discriminative features that achieve lower MAE compared to single feature performance.