Digital Image Processing

Abstract

Face detection and recognition is an effective and popular technique for authorizing the users and securing the data at the time of transactions. However, there exists some other traditional face recognition approaches with some disadvantages when opted to various types of input conditions. They are Geometric, Photometric and Principal Component Analysis etc.

The geometric approach deals with the geometric features that are generated by a sector, edges and regions of some shapes formed by many points. The recognition result is compared by analyzing the featured set. A calculation is done between the features in the template image and   every image in the database. Hence this method is tough. But the main complication is locating a point automatically. Also the other problem arises if the image is of poor quality.

On the other hand the photometric approach refines an image into values and that value is compared with templates to remove variances. It depends on the input image and the geometric location of different angles. While the photometric transformation is employed on the source image, it does not consider the photometric change which is nothing but the changes in the pixel. This approach requires multiple registered images of the same person. If any images which are not present in the dataset are subjected to processing, this approach considers that image as a new image which is specified as unauthenticated.

Principal Component Analysis is an orthogonal linear transformation which maps the data to another coordinates. It uses Eigen faces. This approach processes the images of equal sizes. Also this approach reduces the sizes of data using data compression technique. The images get disintegrated to form unrelated blocks that are stored in one dimension array know as Eigen faces. The face images can be signified as a sum of Eigen faces.

Keywords

Full Text

Abstract

Age estimation is achieved better using facial images. Normally, the age estimation is categorized in two ways such as age estimation and age group classification. The age estimation is performed to estimate the exact age of a given test image. Whereas the age group classification is done to classify the age group of a person such as adults, junior adults and senior adults. The age group classification is supported by the features and patterns which is clearly defined in craniofacial growth. The wrinkles and ratios play an important role in age group classification. These similar features can be also applied for age estimation of an individual. Our proposed methodology estimates the age of a person in three steps. 1. Face detection, 2. Feature extraction, 3. Gender based classification, 4. Age estimation.

Face detection is achieved using Viola-Jones algorithm. After pre-processing and contract enhancement, the proposed facial feature called Bio-Content Feature (BCF) is extracted. The proposed feature is extracted using angle-based and dimension-based measurements and dimension reduction methods. The proposed extraction methodology handles the problems like image misalignment, high dimensionality problems, shape variations and geometrical transformations.

The extracted feature is subjected to multi-linear regression to find the relationship between the test and the trained image. The third step performs gender based classification of the images based on the result of the regression. The fourth step delivers the estimated age of the test image. This proposed work shows its accuracy up to 92% via gender-based classification on age estimation. The advantage of the proposed methodology for gender-based classification is proved by vast experiments on the public available FG-NET database. The approach could be widely used in real world applications, crime investigation, and human-computer interaction.

Keywords

Full Text

References