The first school I attended was "The school of life and hard knocks!" and I came away with a distinction. I then went to a comprehensive school in London. The teachers all agreed that I would never achieve anything. Five years later I left school with seventeen "A" levels, ten "O" levels and I then went on to university (Cambridge) studying medicine and then applied maths. Many years later, I was walking along a beach in New Zealand's South island, when I bumped into one of my old school teachers. She said to me "Did you ever manage to achieve anything because you always appeared to be somewhere else during my classes?" I tried to explain to her my work in the field of algorithms for the development of facial recognition software. Such as.. Those derived from Karhunen-Loeve's transformation. Given an s-dimensional vector representation of each face in a training set of images, Principal Component Analysis (PCA) tends to find a t-dimensional subspace whose basis vectors correspond to the maximum variance direction in the original image space. This new subspace is normally lower dimensional (t<<s). If the image elements are considered as random variables, the PCA basis vectors are defined as eigenvectors of the scatter matrix. Purpose – To study the mathematical image coding approaches used in two types of biometric systems, and the physical nature of those biometrics. Design/methodology/approach – Gives details of algorithms used to encode data from images in established and new automatic iris recognition systems. Then examines face recognition techniques based on geometry, texture and three-dimensional data. Findings – Most commercial iris recognition systems are based on the algorithms developed by one man, John Daugman. Whilst iris systems can be used to check a person's identity against a large database of enrolled people, face recognition systems are currently only capable of use in one-to-one recognition mode, or in identification mode against a very small database. The iris is very distinctive and stable over time, but the face is much more variable and therefore difficult to identify with accuracy. Then she said "You were always a problem child, you never ever listened to anything we taught you!" It does seem quite odd that despite anything, she failed to ever acknowledge anything. My days at school were possibly the very best days of my life. I would love to go back once more just for the sake of it..

1 preschool, 2 elementary, 1 middle, 3 high school, 2 colleges

Two rural schools: One elementary school: One junior High: One senior High: One Trade school: One Trade college: One school of life: (never ending)

4 schools. Two elementary schools, then middle school and high school.

Seven, not including one college, and a University. BUT I learned much more on the streets.