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An augmented approach to detect persons based on face images captured under uncontrolled conditions is a challenging task. We propose Compact Hybrid Domain based Human Recognition using Face Images in this research. The three sets of features viz., Histogram Intensities (HI), Discrete Wavelet Transform (DWT), Double Density Dual Tree Discrete Wavelet Transform (DDDTDWT) are computed. The first set of features is extracted by HI and considered only the dominant 200 out of 256 coefficient values. The second set of features is extracted using DWT and considering only approximation band coefficients and the number of features are only 1/4th of the original size. The third set of features are extracted by DDDTDWT and considered fifth band coefficients as features. The concluding features are obtained by concatenating all the three features which are effective and compressed. The database and the test face image features are matched using Euclidian Distance (ED) to calculate performance parameters. The results of the projected model are superior to the current techniques and also anticipated that the speed of computation in a real-time system is high as the number of features are compressed.