# Improving Juvenile Age Estimation Based on Facial Landmark Points and Gravity Moment
## 1 Introduction  
Age estimation enables the automatic tagging of a person's age with a specific number or age bracket. It is relevant in real-world applications such as web access control [1], criminal investigations [2], forensics [3] and healthcare [4], where it can be particularly useful for addressing the problem of estimating the age of a separated or unaccompanied child [5]. Age estimation systems require inputs of features such as the iris [6], voice [1], teeth [7] or blood [8]. However, the majority of the systems rely on facial images [2, 9, 10]. This is due to the face being the most visible part of the body and natural storage for personal traits, making it the most representative part of an individual. Moreover, it is easy to acquire facial images through the use of portable, non-evasive cameras. Sharing the images is easier due to the widespread availability of the Internet. Consequently, the potential application of automatic age estimation systems continue to attract both researchers and practitioners. However, estimating age from the facial image is challenging due to the variability of the face, which results from intrinsic (genes) and extrinsic (environmental) factors impacting the face in different ways. As a result, aging manifests differently for different individuals, or commonly for age groups. Whereas in the juvenile group, aging manifests by subtle movement and growth of facial bonesand cranial features, aging in adults is perceived through skin texture changes resulting from loss of skin elasticity and skin quality [11]. These cues broadly fall under global and local features corresponding to shape-related features on the one hand, and texture-related features on the other hand [10]. Global features include facial shape geometry and allied anthropometric characteristics that evolve from the childhood stage of growth. The evolution slows down or ceases as the child enters adulthood. Consequently, global features are more useful for juvenile age estimation or classification between juveniles and adults. Conversely, local features begin to manifest at adulthood. They include skin wrinkles and aging spots that together form the texture of the skin. They are more useful for estimating the ages of adults. Although aging introduces a unique set of features for the growing juvenile or aging adult, the frequencies of the features can influence the overall appearance of the face. The representation of the appearance of the face has been the basis for many proposals [12]. Whereas some proposals rely solely on global or local features representations, others consider both global and local features [13; 14]. In general, age estimation systems operate under two different phases: image representation and age prediction. In the representation phase, facial features are extracted and used as input for the system. Some notable representation methods include anthropometric-based models [15], texture-based models [13], Active Appearance Models (AAM) [16], Active Shape Models (ASM) [17], Aging Pattern Subspace (AGES) [18], Age Manifold [19] and Multi-feature Fusion [20]. Other useful methods include Local Binary Pattern (LBP) [13], Gabor Wavelet (GW) [21], Sobel [22] and Canny [23]. The goal of feature representation is to acquire a feature vector that adequately represent the target age or age-group. Some researchers propose methods that utilize the facial landmark points, depicted in Figure 1, to represent aging features. Here, pioneer investigators include [PERSON] et al. [15]. They used geometrical features and wrinkle patterns derived from the locations of the landmark points to represent aging features. Additionally, [PERSON] and [PERSON] [20] proposed a model to examine facial shape differences for children under 18 years of age. Recently, shape-oriented methods like the AAM are proposed for various kinds of representations [14; 16; 24; 25]. Regardless of the adopted representation method, the obtained features should be relevant to the objectives of the proposal.