Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new frequency domain face recognition method using wavelet decomposition and quaternion correlation filters is proposed. The wavelet decomposition of the face image leads to a wavelet subband representation, which contains four subband images corresponding to four orthogonal channels. These four subbands can be encoded into a 2D quaternion number array. The quaternion correlation filter method is developed to perform pattern recognition jointly on multichannel 2D signals. The proposed method has been shown to achieve significant improvement in face recognition results compared to the traditional advanced correlation filter method for handling illumination variations of face images.  experimented with the CMU PIE database consisting of 65 people with 21 illumination variations per person, showing that our method can achieve close to 100% recognition accuracy using just a single training image of a person under neutral frontal lighting and testing on all other unseen harsh illumination conditions.
['Chunyan Xie', 'Marios Savvides', 'Bhagavatula Vijaya Kumar']
I am writing a research paper on a idea Quaternion correlation filters for face recognition in wavelet domain
[{"from": "human", "value": "I am writing a research paper on a idea Quaternion correlation filters for face recognition in wavelet domain"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new frequency domain face recognition method using wavelet decomposition and quaternion correlation filters is proposed. The wavelet decomposition of the face image leads to a wavelet subband representation, which contains four subband images corresponding to four orthogonal channels. These four subbands can be encoded into a 2D quaternion number array. The quaternion correlation filter method is developed to perform pattern recognition jointly on multichannel 2D signals. The proposed method has been shown to achieve significant improvement in face recognition results compared to the traditional advanced correlation filter method for handling illumination variations of face images.  experimented with the CMU PIE database consisting of 65 people with 21 illumination variations per person, showing that our method can achieve close to 100% recognition accuracy using just a single training image of a person under neutral frontal lighting and testing on all other unseen harsh illumination conditions."}]