# Face Gender and Age Classification Based on Multi-Task, Multi-Instance and Multi-Scale Learning
## 1 Introduction  
In daily life, gender and age classification are very important, which can help us to distinguish whether the person we contact is a \"sir\" or \"madam\", as well as \"young\" or \"old\". These behaviors rely heavily on human forecasting and the recognition of facial attributes: gender and age [1]. In addition, the gender and age attributes of faces have other real-world applications. For example, vending machines can deny selling cigarettes to minors and an electronic billboard can display advertisements based on gender and age. However, the performance index of face attribute recognition by machines is far from meeting the needs of commercial applications [2, 3]. In general, face gender and age classification is a branch of face recognition; thus, generic face recognition technology can naturally be applied to this problem [4, 5, 6, 7, 8]. Therefore, most existing methods are based on manually designed features in this field, such as Local Binary Patterns (LBPs) [9], Gabor [10], Biologically Inspired Feature (BIF) [11] and Spatially Flexible Patches (SFP) [12]. After these manually designed feature extractions,we can resort to a classification or regression algorithm to estimate facial gender and age. Among them, Support Vector Machine (SVM)-based approaches [1; 11] are used for gender classification and age grouping; Support Vector Regression (SVR) [13], linear regression [14], Canonical Correlation Analysis (CCA) [15] and Partial Least Squares (PLS) [16] regression methods are available for accurate age estimation. However, these methods can only be applied to the constrained benchmarks, rather than achieving satisfactory results in benchmarks in the wild [1; 17]. Inspired by the success of ImageNet classification and face recognition [18], deep learning has been applied to gender and age classification [2; 3; 19; 20]. [PERSON] et al. [19] extracted discriminant features using CNN and these were combined with classification and regression approaches to estimate age based on FG-NET and MORPH. Additionally, [PERSON] et al. [2] employed deep learning for age and gender classification together based on an uncontrolled Adience database. [PERSON] et al. [21] took full advantage of CNN and SVM to propose a hybrid neural network with better results compared with a plain CNN. [PERSON] et al. [22] found that the hybrid model integrating CNN and Conditional Random Field is better than other methods through extensive image segmentation experiments. What is more, [PERSON] et al. [23] proposed a hybrid neural network by integrating CNN and SVM for scene recognition and domain adaption. [PERSON] et al. [24] proposed a hybrid model by integrating CNN and Random Forest (RF) for facial expression recognition, in which a conditional CNN enhanced the RF for pose-aligned facial expression recognition. Recently, [PERSON] [8] proposed an architecture for age estimation based on a cascade of classification tree ensembles, which have been known recently as a Deep Random Forest (DRF). The model consists of two types of DRF. The first type extends the input facial features; the second fuses all enhanced representations to consider the fuzziness of the face age. Experimental results demonstrated that it can achieve high accuracy and fast convergence with a limited amount of image data, rather than a large amount of data required by a plain CNN. The accurate classification of face gender and age includes two important steps: feature extraction and classifier design, while the former is the key to the whole process. It not only requires the extracted features to have great differences among different classes, but also requires it to maintain invariance within the same class. Most traditional methods use manually designed features and statistical models for the recognition of gender and age [10; 11; 15; 16], which have achieved favorable results based on the benchmarks of controlled databases, such as FG-NET [25] and MORPH [26].