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Alex D.

@alexdonuk

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Experienced iOS, Cocoa, Windows Phone 7, Java, C# .NET and PHP developer

$25 USD / Hour

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United Kingdom (6:20 AM)

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Joined on June 28, 2012

$25 USD / Hour

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I am a UK based developer with 11 years of industry experience. After over 8 years in systems management at a leading UK ISP, I have spent the last 2.5 years as the lead iOS & Windows Phone 7 developer for a rapidly expanding design and development house, delivering turnkey mobile application solutions to clients in a broad range of industries. I am new to freelancing and that is reflected in my hourly rate. I offer a bespoke consultation and development service tailored to your needs. Whether working as part of a wider team or as a sole developer, debugging a script or helping you realise your mobile application vision from the ground up, I pledge to deliver a high quality, professional result.

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Portfolio

Portfolio

179005
Model Potential is a model scouting application for iOS, developed in association with Select Model Management. The app provides a means for aspiring models to submit a profile for consideration by Select's model scouts. The client wanted the app to include face and feature recognition, in order to attempt to determine the attractiveness of the user (rated as Average, Good & Great) prior to their profile being submitted for review. This was accomplished using OpenCV with custom Haar cascade classifiers for more accurate detection of key points on the face, such as the edges of the mouth, tip of the nose and inner and outer edges of the eyes. Ratios are then calculated from measurements between specific points, with the optimum result being a "Golden" ratio of 1:1.618.

Model Potential - iOS model scouting app

Model Potential is a model scouting application for iOS, developed in association with Select Model Management. The app provides a means for aspiring models to submit a profile for consideration by Select's model scouts. The client wanted the app to include face and feature recognition, in order to attempt to determine the attractiveness of the user (rated as Average, Good & Great) prior to their profile being submitted for review. This was accomplished using OpenCV with custom Haar cascade classifiers for more accurate detection of key points on the face, such as the edges of the mouth, tip of the nose and inner and outer edges of the eyes. Ratios are then calculated from measurements between specific points, with the optimum result being a "Golden" ratio of 1:1.618.

Model Potential - iOS model scouting app

Model Potential is a model scouting application for iOS, developed in association with Select Model Management. The app provides a means for aspiring models to submit a profile for consideration by Select's model scouts. The client wanted the app to include face and feature recognition, in order to attempt to determine the attractiveness of the user (rated as Average, Good & Great) prior to their profile being submitted for review. This was accomplished using OpenCV with custom Haar cascade classifiers for more accurate detection of key points on the face, such as the edges of the mouth, tip of the nose and inner and outer edges of the eyes. Ratios are then calculated from measurements between specific points, with the optimum result being a "Golden" ratio of 1:1.618.

Model Potential - iOS model scouting app

Model Potential is a model scouting application for iOS, developed in association with Select Model Management. The app provides a means for aspiring models to submit a profile for consideration by Select's model scouts. The client wanted the app to include face and feature recognition, in order to attempt to determine the attractiveness of the user (rated as Average, Good & Great) prior to their profile being submitted for review. This was accomplished using OpenCV with custom Haar cascade classifiers for more accurate detection of key points on the face, such as the edges of the mouth, tip of the nose and inner and outer edges of the eyes. Ratios are then calculated from measurements between specific points, with the optimum result being a "Golden" ratio of 1:1.618.

Model Potential - iOS model scouting app

Model Potential is a model scouting application for iOS, developed in association with Select Model Management. The app provides a means for aspiring models to submit a profile for consideration by Select's model scouts. The client wanted the app to include face and feature recognition, in order to attempt to determine the attractiveness of the user (rated as Average, Good & Great) prior to their profile being submitted for review. This was accomplished using OpenCV with custom Haar cascade classifiers for more accurate detection of key points on the face, such as the edges of the mouth, tip of the nose and inner and outer edges of the eyes. Ratios are then calculated from measurements between specific points, with the optimum result being a "Golden" ratio of 1:1.618.

Model Potential - iOS model scouting app

179001
Celebalike is a hugely popular iOS entertainment application that has frequently featured in the top 10 charts in both the UK and US stores. The app uses face recognition to match user-submitted pictures against a database of celebrity faces, returning the highest matches. This is accomplished by uploading image data from the app to a server via a PHP web service. Processing is then handed off to C++ application linking against the commercial FaceSDK face recognition library by Luxand. I have been the sole developer contributing to the client-side Objective-C and server-side C++ portions of the application since its original release in 2010.

Celebalike iOS app

Celebalike is a hugely popular iOS entertainment application that has frequently featured in the top 10 charts in both the UK and US stores. The app uses face recognition to match user-submitted pictures against a database of celebrity faces, returning the highest matches. This is accomplished by uploading image data from the app to a server via a PHP web service. Processing is then handed off to C++ application linking against the commercial FaceSDK face recognition library by Luxand. I have been the sole developer contributing to the client-side Objective-C and server-side C++ portions of the application since its original release in 2010.

Celebalike iOS app

Celebalike is a hugely popular iOS entertainment application that has frequently featured in the top 10 charts in both the UK and US stores. The app uses face recognition to match user-submitted pictures against a database of celebrity faces, returning the highest matches. This is accomplished by uploading image data from the app to a server via a PHP web service. Processing is then handed off to C++ application linking against the commercial FaceSDK face recognition library by Luxand. I have been the sole developer contributing to the client-side Objective-C and server-side C++ portions of the application since its original release in 2010.

Celebalike iOS app

Celebalike is a hugely popular iOS entertainment application that has frequently featured in the top 10 charts in both the UK and US stores. The app uses face recognition to match user-submitted pictures against a database of celebrity faces, returning the highest matches. This is accomplished by uploading image data from the app to a server via a PHP web service. Processing is then handed off to C++ application linking against the commercial FaceSDK face recognition library by Luxand. I have been the sole developer contributing to the client-side Objective-C and server-side C++ portions of the application since its original release in 2010.

Celebalike iOS app

175500
An iOS entertainment application written in Objective-C and C. Utilises OpenCV for face recognition and feature detection, animations in Core Animation, a custom camera interface using the AV Foundation APIs and social media integration.

Oh!Meter iOS app

An iOS entertainment application written in Objective-C and C. Utilises OpenCV for face recognition and feature detection, animations in Core Animation, a custom camera interface using the AV Foundation APIs and social media integration.

Oh!Meter iOS app

An iOS entertainment application written in Objective-C and C. Utilises OpenCV for face recognition and feature detection, animations in Core Animation, a custom camera interface using the AV Foundation APIs and social media integration.

Oh!Meter iOS app

An iOS entertainment application written in Objective-C and C. Utilises OpenCV for face recognition and feature detection, animations in Core Animation, a custom camera interface using the AV Foundation APIs and social media integration.

Oh!Meter iOS app

179005
Model Potential is a model scouting application for iOS, developed in association with Select Model Management. The app provides a means for aspiring models to submit a profile for consideration by Select's model scouts. The client wanted the app to include face and feature recognition, in order to attempt to determine the attractiveness of the user (rated as Average, Good & Great) prior to their profile being submitted for review. This was accomplished using OpenCV with custom Haar cascade classifiers for more accurate detection of key points on the face, such as the edges of the mouth, tip of the nose and inner and outer edges of the eyes. Ratios are then calculated from measurements between specific points, with the optimum result being a "Golden" ratio of 1:1.618.

Model Potential - iOS model scouting app

Model Potential is a model scouting application for iOS, developed in association with Select Model Management. The app provides a means for aspiring models to submit a profile for consideration by Select's model scouts. The client wanted the app to include face and feature recognition, in order to attempt to determine the attractiveness of the user (rated as Average, Good & Great) prior to their profile being submitted for review. This was accomplished using OpenCV with custom Haar cascade classifiers for more accurate detection of key points on the face, such as the edges of the mouth, tip of the nose and inner and outer edges of the eyes. Ratios are then calculated from measurements between specific points, with the optimum result being a "Golden" ratio of 1:1.618.

Model Potential - iOS model scouting app

Model Potential is a model scouting application for iOS, developed in association with Select Model Management. The app provides a means for aspiring models to submit a profile for consideration by Select's model scouts. The client wanted the app to include face and feature recognition, in order to attempt to determine the attractiveness of the user (rated as Average, Good & Great) prior to their profile being submitted for review. This was accomplished using OpenCV with custom Haar cascade classifiers for more accurate detection of key points on the face, such as the edges of the mouth, tip of the nose and inner and outer edges of the eyes. Ratios are then calculated from measurements between specific points, with the optimum result being a "Golden" ratio of 1:1.618.

Model Potential - iOS model scouting app

Model Potential is a model scouting application for iOS, developed in association with Select Model Management. The app provides a means for aspiring models to submit a profile for consideration by Select's model scouts. The client wanted the app to include face and feature recognition, in order to attempt to determine the attractiveness of the user (rated as Average, Good & Great) prior to their profile being submitted for review. This was accomplished using OpenCV with custom Haar cascade classifiers for more accurate detection of key points on the face, such as the edges of the mouth, tip of the nose and inner and outer edges of the eyes. Ratios are then calculated from measurements between specific points, with the optimum result being a "Golden" ratio of 1:1.618.

Model Potential - iOS model scouting app

Model Potential is a model scouting application for iOS, developed in association with Select Model Management. The app provides a means for aspiring models to submit a profile for consideration by Select's model scouts. The client wanted the app to include face and feature recognition, in order to attempt to determine the attractiveness of the user (rated as Average, Good & Great) prior to their profile being submitted for review. This was accomplished using OpenCV with custom Haar cascade classifiers for more accurate detection of key points on the face, such as the edges of the mouth, tip of the nose and inner and outer edges of the eyes. Ratios are then calculated from measurements between specific points, with the optimum result being a "Golden" ratio of 1:1.618.

Model Potential - iOS model scouting app

179001
Celebalike is a hugely popular iOS entertainment application that has frequently featured in the top 10 charts in both the UK and US stores. The app uses face recognition to match user-submitted pictures against a database of celebrity faces, returning the highest matches. This is accomplished by uploading image data from the app to a server via a PHP web service. Processing is then handed off to C++ application linking against the commercial FaceSDK face recognition library by Luxand. I have been the sole developer contributing to the client-side Objective-C and server-side C++ portions of the application since its original release in 2010.

Celebalike iOS app

Celebalike is a hugely popular iOS entertainment application that has frequently featured in the top 10 charts in both the UK and US stores. The app uses face recognition to match user-submitted pictures against a database of celebrity faces, returning the highest matches. This is accomplished by uploading image data from the app to a server via a PHP web service. Processing is then handed off to C++ application linking against the commercial FaceSDK face recognition library by Luxand. I have been the sole developer contributing to the client-side Objective-C and server-side C++ portions of the application since its original release in 2010.

Celebalike iOS app

Celebalike is a hugely popular iOS entertainment application that has frequently featured in the top 10 charts in both the UK and US stores. The app uses face recognition to match user-submitted pictures against a database of celebrity faces, returning the highest matches. This is accomplished by uploading image data from the app to a server via a PHP web service. Processing is then handed off to C++ application linking against the commercial FaceSDK face recognition library by Luxand. I have been the sole developer contributing to the client-side Objective-C and server-side C++ portions of the application since its original release in 2010.

Celebalike iOS app

Celebalike is a hugely popular iOS entertainment application that has frequently featured in the top 10 charts in both the UK and US stores. The app uses face recognition to match user-submitted pictures against a database of celebrity faces, returning the highest matches. This is accomplished by uploading image data from the app to a server via a PHP web service. Processing is then handed off to C++ application linking against the commercial FaceSDK face recognition library by Luxand. I have been the sole developer contributing to the client-side Objective-C and server-side C++ portions of the application since its original release in 2010.

Celebalike iOS app

175500
An iOS entertainment application written in Objective-C and C. Utilises OpenCV for face recognition and feature detection, animations in Core Animation, a custom camera interface using the AV Foundation APIs and social media integration.

Oh!Meter iOS app

An iOS entertainment application written in Objective-C and C. Utilises OpenCV for face recognition and feature detection, animations in Core Animation, a custom camera interface using the AV Foundation APIs and social media integration.

Oh!Meter iOS app

An iOS entertainment application written in Objective-C and C. Utilises OpenCV for face recognition and feature detection, animations in Core Animation, a custom camera interface using the AV Foundation APIs and social media integration.

Oh!Meter iOS app

An iOS entertainment application written in Objective-C and C. Utilises OpenCV for face recognition and feature detection, animations in Core Animation, a custom camera interface using the AV Foundation APIs and social media integration.

Oh!Meter iOS app

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Experience

Domain Systems Manager

Aug, 2001 - Dec, 2009

•

8 years, 4 months

Namesco Limited

Aug, 2001 - Dec, 2009

•

8 years, 4 months

Namesco Limited (Names.co Internet Plc prior to 2004) is a leading UK provider of domain name registration, email and hosting services and the 6th largest Nominet UK domain registrar by volume. In this role, I was responsible for data integrity and the ongoing development and management of the company's automated domain name provisioning system. This primarily involved developing back-end code in PHP, Java and C, in addition to developing and maintaining related front-end UI tools in both the custo

Aug, 2001 - Dec, 2009

•

8 years, 4 months

Assistant Projects Editor

May, 2000 - Jul, 2001

•

1 year, 2 months

Style Index Ltd

May, 2000 - Jul, 2001

•

1 year, 2 months

Style Index was a small, family-run design and development house, specialising in print design and database-driven e-commerce sites. My role involved Perl scripting, web design, database design and client liaison. One stand out client project to which I contributed was the MEB (Midlands Electricity Board) Electricity Supply Handbook 2000, a CD-based interactive product built using Macromedia Director and a 4th Dimension database.

May, 2000 - Jul, 2001

•

1 year, 2 months

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